conference
stringclasses 6
values | title
stringlengths 2
176
| abstract
stringlengths 2
5k
| decision
stringclasses 11
values |
|---|---|---|---|
ICLR.cc/2021/Conference | GAN "Steerability" without optimization | Recent research has shown remarkable success in revealing "steering" directions in the latent spaces of pre-trained GANs. These directions correspond to semantically meaningful image transformations (e.g., shift, zoom, color manipulations), and have the same interpretable effect across all categories that the GAN can generate. Some methods focus on user-specified transformations, while others discover transformations in an unsupervised manner. However, all existing techniques rely on an optimization procedure to expose those directions, and offer no control over the degree of allowed interaction between different transformations. In this paper, we show that "steering" trajectories can be computed in closed form directly from the generator's weights without any form of training or optimization. This applies to user-prescribed geometric transformations, as well as to unsupervised discovery of more complex effects. Our approach allows determining both linear and nonlinear trajectories, and has many advantages over previous methods. In particular, we can control whether one transformation is allowed to come on the expense of another (e.g., zoom-in with or without allowing translation to keep the object centered). Moreover, we can determine the natural end-point of the trajectory, which corresponds to the largest extent to which a transformation can be applied without incurring degradation. Finally, we show how transferring attributes between images can be achieved without optimization, even across different categories.
| Accept (Spotlight) |
ICLR.cc/2020/Conference | Progressive Compressed Records: Taking a Byte Out of Deep Learning Data | Deep learning training accesses vast amounts of data at high velocity, posing challenges for datasets retrieved over commodity networks and storage devices. We introduce a way to dynamically reduce the overhead of fetching and transporting training data with a method we term Progressive Compressed Records (PCRs). PCRs deviate from previous formats by leveraging progressive compression to split each training example into multiple examples of increasingly higher fidelity, without adding to the total data size. Training examples of similar fidelity are grouped together, which reduces both the system overhead and data bandwidth needed to train a model. We show that models can be trained on aggressively compressed representations of the training data and still retain high accuracy, and that PCRs can enable a 2x speedup on average over baseline formats using JPEG compression. Our results hold across deep learning architectures for a wide range of datasets: ImageNet, HAM10000, Stanford Cars, and CelebA-HQ. | Reject |
ICLR.cc/2023/Conference | The Devil is in the Wrongly-classified Samples: Towards Unified Open-set Recognition | Open-set Recognition (OSR) aims to identify test samples whose classes are not seen during the training process. Recently, Unified Open-set Recognition (UOSR) has been proposed to reject not only unknown samples but also known but wrongly classified samples, which tends to be more practical in real-world applications. In this paper, we deeply analyze the UOSR task under different training and evaluation settings to shed light on this promising research direction. For this purpose, we first evaluate the UOSR performance of several OSR methods and show a significant finding that the uncertainty distribution of almost all these methods is actually closer to the expectation of UOSR than OSR. We show that the reason lies in the known but wrongly classified samples, as their uncertainty distribution is extremely close to unknown samples rather than known and correctly classified samples. Second, we analyze how the two training settings of OSR (i.e., pre-training and outlier exposure) influence the UOSR. We find although they are both beneficial for distinguishing known and correctly classified samples from unknown samples, pre-training is also helpful for identifying known but wrongly classified samples while outlier exposure is not. In addition to different training settings, we also formulate a new evaluation setting for UOSR which is called few-shot UOSR, where only one or five samples per unknown class are available during evaluation to help identify unknown samples. We propose FS-KNNS for the few-shot UOSR to achieve state-of-the-art performance under all settings. | Accept: poster |
ICLR.cc/2022/Conference | Pairwise Adversarial Training for Unsupervised Class-imbalanced Domain Adaptation | Unsupervised domain adaptation (UDA) has become an appealing approach for knowledge transfer from a labeled source domain to an unlabeled target domain. However, when the classes in source and target domains are imbalanced, most existing UDA methods experience significant performance drop, as the decision boundary usually favors the majority classes. Some recent class-imbalanced domain adaptation (CDA) methods aim to tackle the challenge of biased label distribution by exploiting pseudo-labeled target data during training process. However, these methods may be challenged with the problem of unreliable pseudo labels and error accumulation during training. In this paper, we propose a pairwise adversarial training approach to augment training data for unsupervised class-imbalanced domain adaptation. Unlike conventional adversarial training in which the adversarial samples are obtained from the $\ell_p$ ball of the original data, we obtain the semantic adversarial samples from the interpolated line of the aligned pair-wise samples from source domain and target domain. Experimental results and ablation study show that our method can achieve considerable improvements on the CDA benchmarks compared with the state-of-art methods focusing on the same problem.
| Reject |
ICLR.cc/2018/Conference | Time Limits in Reinforcement Learning | In reinforcement learning, it is common to let an agent interact with its environment for a fixed amount of time before resetting the environment and repeating the process in a series of episodes. The task that the agent has to learn can either be to maximize its performance over (i) that fixed amount of time, or (ii) an indefinite period where the time limit is only used during training. In this paper, we investigate theoretically how time limits could effectively be handled in each of the two cases. In the first one, we argue that the terminations due to time limits are in fact part of the environment, and propose to include a notion of the remaining time as part of the agent's input. In the second case, the time limits are not part of the environment and are only used to facilitate learning. We argue that such terminations should not be treated as environmental ones and propose a method, specific to value-based algorithms, that incorporates this insight by continuing to bootstrap at the end of each partial episode. To illustrate the significance of our proposals, we perform several experiments on a range of environments from simple few-state transition graphs to complex control tasks, including novel and standard benchmark domains. Our results show that the proposed methods improve the performance and stability of existing reinforcement learning algorithms. | Reject |
ICLR.cc/2023/Conference | Delving into Discrete Normalizing Flows on SO(3) Manifold for Probabilistic Rotation Modeling | Normalizing flows (NFs) provide a powerful tool to construct an expressive distribution by a sequence of trackable transformations of a base distribution and form a probabilistic model of underlying data. Rotation, as an important quantity in computer vision, graphics and robotics, can exhibit many ambiguities when occlusion and symmetry occur and thus demands such probabilistic models. Though various NFs in Euclidean space have been proposed, there are no normalizing flows tailored for SO(3) manifold. Given the unique non-Euclidean properties of the rotation manifold, adapting the existing NFs to SO(3) manifold is non-trivial. In this paper, we propose a novel normalizing flow on SO(3) by combining a Möbius transformation-based layer and a quaternion affine transformation. With our proposed rotation normalizing flows, one can not only effectively express arbitrary distributions on SO(3), but also conditionally build the target distribution given input observations. Extensive experiments show that our rotation normalizing flows significantly outperform the baselines on both unconditional and conditional tasks. | Withdrawn |
ICLR.cc/2022/Conference | Back to Basics: Efficient Network Compression via IMP | Network pruning is a widely used technique for effectively compressing Deep Neural Networks with little to no degradation in performance during inference. Iterative Magnitude Pruning (IMP) (Han et al., 2015) is one of the most established approaches for network pruning, consisting of several iterative training and pruning steps, where a significant amount of the network’s performance is lost after pruning and then recovered in the subsequent retraining phase. While commonly used as a benchmark reference, it is often argued that a) it reaches suboptimal states by not incorporating sparsification into the training phase, b) its global selection criterion fails to properly determine optimal layer-wise pruning rates and c) its iterative nature makes it slow and non-competitive. In light of recently proposed retraining techniques, we investigate these claims through rigorous and consistent experiments where we compare IMP to pruning-during-training algorithms, evaluate proposed modifications of its selection criterion and study the number of iterations and total training time actually required. We find that IMP with SLR (Le & Hua, 2021) for retraining can outperform state-of-the-art pruning-during-training approaches without or with only little computational overhead, that the global magnitude selection criterion is largely competitive with more complex approaches and that only few retraining epochs are needed in practice to achieve most of the sparsity-vs.-performance trade-off of IMP. Our goals are both to demonstrate that basic IMP can already provide state-of-the-art pruning results on par or outperforming more complex or heavily parameterized approaches and also to establish a more realistic yet easily realisable baseline for future research. | Reject |
ICLR.cc/2023/Conference | Fine-Tuning Offline Policies With Optimistic Action Selection | Offline reinforcement learning algorithms can train performant policies for hard tasks using previously-collected datasets. However, the quality of the offline dataset often limits the levels of performance possible. We consider the problem of improving offline policies through online fine-tuning. Offline RL requires a pessimistic training objective to mitigate distributional shift between the trained policy and the offline behavior policy, which will make the trained policy averse to picking novel actions. In contrast, online RL requires exploration, or optimism. Thus, fine-tuning online policies with the offline training objective is not ideal. Additionally, loosening the fine-tuning objective to allow for more exploration can potentially destroy the behaviors learned in the offline phase because of the sudden and significant change in the optimization objective. To mitigate this challenge, we propose a method to facilitate exploration during online fine-tuning that maintains the same training objective throughout both offline and online phases, while encouraging exploration. We accomplish this by changing the action-selection method to be more optimistic with respect to the Q-function. By choosing to take actions in the environment with higher expected Q-values, our method is able to explore and improve behaviors more efficiently, obtaining 56% more returns on average than the alternative approaches on several locomotion, navigation, and manipulation tasks. | Reject |
ICLR.cc/2022/Conference | On Optimal Early Stopping: Overparametrization versus Underparametrization | Early stopping is a simple and widely used method to prevent over-training neural networks. We develop theoretical results to reveal the relationship between optimal early stopping time and model dimension as well as sample size of the dataset for certain linear regression models. Our results demonstrate two very different behaviors when the model dimension exceeds the number of features versus the opposite scenario. While most previous works on linear models focus on the latter setting, we observe that in common deep learning tasks, the dimension of the model often exceeds the number of features arising from data. We demonstrate experimentally that our theoretical results on optimal early stopping time corresponds to the training process of deep neural network. Moreover, we study the effect of early stopping on generalization and demonstrate that optimal early stopping can help mitigate ''descent'' in various settings. | Reject |
ICLR.cc/2020/Conference | Watch the Unobserved: A Simple Approach to Parallelizing Monte Carlo Tree Search | Monte Carlo Tree Search (MCTS) algorithms have achieved great success on many challenging benchmarks (e.g., Computer Go). However, they generally require a large number of rollouts, making their applications costly. Furthermore, it is also extremely challenging to parallelize MCTS due to its inherent sequential nature: each rollout heavily relies on the statistics (e.g., node visitation counts) estimated from previous simulations to achieve an effective exploration-exploitation tradeoff. In spite of these difficulties, we develop an algorithm, WU-UCT, to effectively parallelize MCTS, which achieves linear speedup and exhibits only limited performance loss with an increasing number of workers. The key idea in WU-UCT is a set of statistics that we introduce to track the number of on-going yet incomplete simulation queries (named as unobserved samples). These statistics are used to modify the UCT tree policy in the selection steps in a principled manner to retain effective exploration-exploitation tradeoff when we parallelize the most time-consuming expansion and simulation steps. Experiments on a proprietary benchmark and the Atari Game benchmark demonstrate the linear speedup and the superior performance of WU-UCT comparing to existing techniques. | Accept (Talk) |
ICLR.cc/2019/Conference | Spectral Inference Networks: Unifying Deep and Spectral Learning | We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization. Spectral Inference Networks generalize Slow Feature Analysis to generic symmetric operators, and are closely related to Variational Monte Carlo methods from computational physics. As such, they can be a powerful tool for unsupervised representation learning from video or graph-structured data. We cast training Spectral Inference Networks as a bilevel optimization problem, which allows for online learning of multiple eigenfunctions. We show results of training Spectral Inference Networks on problems in quantum mechanics and feature learning for videos on synthetic datasets. Our results demonstrate that Spectral Inference Networks accurately recover eigenfunctions of linear operators and can discover interpretable representations from video in a fully unsupervised manner. | Accept (Poster) |
ICLR.cc/2022/Conference | Online Hyperparameter Meta-Learning with Hypergradient Distillation | Many gradient-based meta-learning methods assume a set of parameters that do not participate in inner-optimization, which can be considered as hyperparameters. Although such hyperparameters can be optimized using the existing gradient-based hyperparameter optimization (HO) methods, they suffer from the following issues. Unrolled differentiation methods do not scale well to high-dimensional hyperparameters or horizon length, Implicit Function Theorem (IFT) based methods are restrictive for online optimization, and short horizon approximations suffer from short horizon bias. In this work, we propose a novel HO method that can overcome these limitations, by approximating the second-order term with knowledge distillation. Specifically, we parameterize a single Jacobian-vector product (JVP) for each HO step and minimize the distance from the true second-order term. Our method allows online optimization and also is scalable to the hyperparameter dimension and the horizon length. We demonstrate the effectiveness of our method on three different meta-learning methods and two benchmark datasets. | Accept (Spotlight) |
ICLR.cc/2023/Conference | FedPD: Defying data heterogeneity through privacy distillation | Model performance of federated learning (FL) typically suffers from data heterogeneity, i.e., data distribution varies with clients. Advanced works have already shown great potential for sharing client information to mitigate data heterogeneity. Yet, some literature shows a dilemma in preserving strong privacy and promoting model performance simultaneously. Revisiting the purpose of sharing information motivates us to raise the fundamental questions: Which part of the data is more critical for model generalization? Which part of the data is more privacy-sensitive? Can we solve this dilemma by sharing useful (for generalization) features and maintaining more sensitive data locally? Our work sheds light on data-dominated sharing and training, in a way that we decouple original training data into sensitive features and generalizable features. To be specific, we propose a \textbf{Fed}erated \textbf{P}rivacy \textbf{D}istillation framework named FedPD to alleviate the privacy-performance dilemma. Namely, FedPD keeps the distilled sensitive features locally and constructs a global dataset using shared generalizable features in a differentially private manner. Accordingly, clients can perform local training on both the local and securely shared data for acquiring high model performance and avoiding the leakage of not distilled privacy. Theoretically, we demonstrate the superiority of the sharing-only useful feature strategy over sharing raw data. Empirically, we show the efficacy of FedPD in promoting performance with comprehensive experiments. | Reject |
ICLR.cc/2020/Conference | Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks | In this paper, we present a general framework for distilling expectations with respect to the Bayesian posterior distribution of a deep neural network, significantly extending prior work on a method known as ``Bayesian Dark Knowledge." Our generalized framework applies to the case of classification models and takes as input the architecture of a ``teacher" network, a general posterior expectation of interest, and the architecture of a ``student" network. The distillation method performs an online compression of the selected posterior expectation using iteratively generated Monte Carlo samples from the parameter posterior of the teacher model. We further consider the problem of optimizing the student model architecture with respect to an accuracy-speed-storage trade-off. We present experimental results investigating multiple data sets, distillation targets, teacher model architectures, and approaches to searching for student model architectures. We establish the key result that distilling into a student model with an architecture that matches the teacher, as is done in Bayesian Dark Knowledge, can lead to sub-optimal performance. Lastly, we show that student architecture search methods can identify student models with significantly improved performance. | Reject |
ICLR.cc/2021/Conference | Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? | Robustness to adversarial perturbations and accurate uncertainty estimation are crucial for reliable application of deep learning in real world settings. Dirichlet-based uncertainty (DBU) models are a family of models that predict the parameters of a Dirichlet distribution (instead of a categorical one) and promise to signal when not to trust their predictions. Untrustworthy predictions are obtained on unknown or ambiguous samples and marked with a high uncertainty by the models.
In this work, we show that DBU models with standard training are not robust w.r.t. three important tasks in the field of uncertainty estimation. First, we evaluate how useful the uncertainty estimates are to (1) indicate correctly classified samples. Our results show that while they are a good indicator on unperturbed data, performance on perturbed data decreases dramatically. (2) We evaluate if uncertainty estimates are able to detect adversarial examples that try to fool classification. It turns out that uncertainty estimates are able to detect FGSM attacks but not able to detect PGD attacks. We further evaluate the reliability of DBU models on the task of (3) distinguishing between in-distribution (ID) and out-of-distribution (OOD) data. To this end, we present the first study of certifiable robustness for DBU models. Furthermore, we propose novel uncertainty attacks that fool models into assigning high confidence to OOD data and low confidence to ID data, respectively.
Both approaches show that detecting OOD samples and distinguishing between ID-data and OOD-data is not robust.
Based on our results, we explore the first approaches to make DBU models more robust. We use adversarial training procedures based on label attacks, uncertainty attacks, or random noise and demonstrate how they affect robustness of DBU models on ID data and OOD data. | Reject |
ICLR.cc/2023/Conference | Dataset Pruning: Reducing Training Data by Examining Generalization Influence | The great success of deep learning heavily relies on increasingly larger training data, which comes at a price of huge computational and infrastructural costs. This poses crucial questions that, do all training data contribute to model's performance? How much does each individual training sample or a sub-training-set affect the model's generalization, and how to construct the smallest subset from the entire training data as a proxy training set without significantly sacrificing the model's performance? To answer these, we propose dataset pruning, an optimization-based sample selection method that can (1) examine the influence of removing a particular set of training samples on model's generalization ability with theoretical guarantee, and (2) construct the smallest subset of training data that yields strictly constrained generalization gap. The empirically observed generalization gap of dataset pruning is substantially consistent with our theoretical expectations. Furthermore, the proposed method prunes 40% training examples on the CIFAR-10 dataset, halves the convergence time with only 1.3% test accuracy decrease, which is superior to previous score-based sample selection methods. | Accept: poster |
ICLR.cc/2019/Conference | Graph Generation via Scattering | Generative networks have made it possible to generate meaningful signals such as images and texts from simple noise. Recently, generative methods based on GAN and VAE were developed for graphs and graph signals. However, the mathematical properties of these methods are unclear, and training good generative models is difficult. This work proposes a graph generation model that uses a recent adaptation of Mallat's scattering transform to graphs. The proposed model is naturally composed of an encoder and a decoder. The encoder is a Gaussianized graph scattering transform, which is robust to signal and graph manipulation. The decoder is a simple fully connected network that is adapted to specific tasks, such as link prediction, signal generation on graphs and full graph and signal generation. The training of our proposed system is efficient since it is only applied to the decoder and the hardware requirement is moderate. Numerical results demonstrate state-of-the-art performance of the proposed system for both link prediction and graph and signal generation. These results are in contrast to experience with Euclidean data, where it is difficult to form a generative scattering network that performs as well as state-of-the-art methods. We believe that this is because of the discrete and simpler nature of graph applications, unlike the more complex and high-frequency nature of Euclidean data, in particular, of some natural images. | Reject |
ICLR.cc/2020/Conference | Variational Diffusion Autoencoders with Random Walk Sampling | Variational inference (VI) methods and especially variational autoencoders (VAEs) specify scalable generative models that enjoy an intuitive connection to manifold learning --- with many default priors the posterior/likelihood pair $q(z|x)$/$p(x|z)$ can be viewed as an approximate homeomorphism (and its inverse) between the data manifold and a latent Euclidean space. However, these approximations are well-documented to become degenerate in training. Unless the subjective prior is carefully chosen, the topologies of the prior and data distributions often will not match.
Conversely, diffusion maps (DM) automatically \textit{infer} the data topology and enjoy a rigorous connection to manifold learning, but do not scale easily or provide the inverse homeomorphism.
In this paper, we propose \textbf{a)} a principled measure for recognizing the mismatch between data and latent distributions and \textbf{b)} a method that combines the advantages of variational inference and diffusion maps to learn a homeomorphic generative model. The measure, the \textit{locally bi-Lipschitz property}, is a sufficient condition for a homeomorphism and easy to compute and interpret. The method, the \textit{variational diffusion autoencoder} (VDAE), is a novel generative algorithm that first infers the topology of the data distribution, then models a diffusion random walk over the data. To achieve efficient computation in VDAEs, we use stochastic versions of both variational inference and manifold learning optimization. We prove approximation theoretic results for the dimension dependence of VDAEs, and that locally isotropic sampling in the latent space results in a random walk over the reconstructed manifold.
Finally, we demonstrate the utility of our method on various real and synthetic datasets, and show that it exhibits performance superior to other generative models. | Reject |
ICLR.cc/2023/Conference | Deep Power Laws for Hyperparameter Optimization | Hyperparameter optimization is an important subfield of machine learning that focuses on tuning the hyperparameters of a chosen algorithm to achieve peak performance. Recently, there has been a stream of methods that tackle the issue of hyperparameter optimization, however, most of the methods do not exploit the scaling law property of learning curves. In this work, we propose Deep Power Law (DPL), a neural network model conditioned to yield predictions that follow a power-law scaling pattern. Our model dynamically decides which configurations to pause and train incrementally by making use of multi-fidelity estimation. We compare our method against 7 state-of-the-art competitors on 3 benchmarks related to tabular, image, and NLP datasets covering 59 diverse search spaces. Our method achieves the best results across all benchmarks by obtaining the best any-time results compared to all competitors. | Reject |
ICLR.cc/2020/Conference | Generalized Convolutional Forest Networks for Domain Generalization and Visual Recognition | When constructing random forests, it is of prime importance to ensure high accuracy and low correlation of individual tree classifiers for good performance. Nevertheless, it is typically difficult for existing random forest methods to strike a good balance between these conflicting factors. In this work, we propose a generalized convolutional forest networks to learn a feature space to maximize the strength of individual tree classifiers while minimizing the respective correlation. The feature space is iteratively constructed by a probabilistic triplet sampling method based on the distribution obtained from the splits of the random forest. The sampling process is designed to pull the data of the same label together for higher strength and push away the data frequently falling to the same leaf nodes. We perform extensive experiments on five image classification and two domain generalization datasets with ResNet-50 and DenseNet-161 backbone networks. Experimental results show that the proposed algorithm performs favorably against state-of-the-art methods. | Accept (Poster) |
ICLR.cc/2021/Conference | AR-ELBO: Preventing Posterior Collapse Induced by Oversmoothing in Gaussian VAE | Variational autoencoders (VAEs) often suffer from posterior collapse, which is a phenomenon that the learned latent space becomes uninformative. This is related to local optima introduced by a fixed hyperparameter resembling the data variance in the objective function. We suggest that this variance parameter regularizes the VAE and affects its smoothness, which is the magnitude of its gradient. An inappropriate choice of this parameter causes oversmoothness and leads to posterior collapse. This is shown theoretically by analysis on the linear approximated objective function and empirically in general cases. We propose AR-ELBO, which stands for adaptively regularized ELBO~(Evidence Lower BOund). It controls the strength of regularization by adapting the variance parameter, and thus avoids oversmoothing the model. Generation models trained by proposed objectives show improved Fréchet inception distance~(FID) of images generated from the MNIST and CelebA datasets. | Reject |
ICLR.cc/2021/Conference | Differentiable Weighted Finite-State Transducers | We introduce a framework for automatic differentiation with weighted finite-state transducers (WFSTs) allowing them to be used dynamically at training time. Through the separation of graphs from operations on graphs, this framework enables the exploration of new structured loss functions which in turn eases the encoding of prior knowledge into learning algorithms. We show how the framework can combine pruning and back-off in transition models with various sequence-level loss functions. We also show how to learn over the latent decomposition of phrases into word pieces. Finally, to demonstrate that WFSTs can be used in the interior of a deep neural network, we propose a convolutional WFST layer which maps lower-level representations to higher-level representations and can be used as a drop-in replacement for a traditional convolution. We validate these algorithms with experiments in handwriting recognition and speech recognition. | Reject |
ICLR.cc/2020/Conference | A Simple Recurrent Unit with Reduced Tensor Product Representations | Widely used recurrent units, including Long-short Term Memory (LSTM) and Gated Recurrent Unit (GRU), perform well on natural language tasks, but their ability to learn structured representations is still questionable. Exploiting reduced Tensor Product Representations (TPRs) --- distributed representations of symbolic structure in which vector-embedded symbols are bound to vector-embedded structural positions --- we propose the TPRU, a simple recurrent unit that, at each time step, explicitly executes structural-role binding and unbinding operations to incorporate structural information into learning. The gradient analysis of our proposed TPRU is conducted to support our model design, and its performance on multiple datasets shows the effectiveness of it. Furthermore, observations on linguistically grounded study demonstrate the interpretability of our TPRU. | Reject |
ICLR.cc/2022/Conference | Finding an Unsupervised Image Segmenter in each of your Deep Generative Models | Recent research has shown that numerous human-interpretable directions exist in the latent space of GANs. In this paper, we develop an automatic procedure for finding directions that lead to foreground-background image separation, and we use these directions to train an image segmentation model without human supervision. Our method is generator-agnostic, producing strong segmentation results with a wide range of different GAN architectures. Furthermore, by leveraging GANs pretrained on large datasets such as ImageNet, we are able to segment images from a range of domains without further training or finetuning. Evaluating our method on image segmentation benchmarks, we compare favorably to prior work while using neither human supervision nor access to the training data. Broadly, our results demonstrate that automatically extracting foreground-background structure from pretrained deep generative models can serve as a remarkably effective substitute for human supervision. | Accept (Poster) |
ICLR.cc/2018/Conference | Adversarial Spheres | State of the art computer vision models have been shown to be vulnerable to small adversarial perturbations of the input. In other words, most images in the data distribution are both correctly classified by the model and are very close to a visually similar misclassified image. Despite substantial research interest, the cause of the phenomenon is still poorly understood and remains unsolved. We hypothesize that this counter intuitive behavior is a naturally occurring result of the high dimensional geometry of the data manifold. As a first step towards exploring this hypothesis, we study a simple synthetic dataset of classifying between two concentric high dimensional spheres. For this dataset we show a fundamental tradeoff between the amount of test error and the average distance to nearest error. In particular, we prove that any model which misclassifies a small constant fraction of a sphere will be vulnerable to adversarial perturbations of size $O(1/\sqrt{d})$. Surprisingly, when we train several different architectures on this dataset, all of their error sets naturally approach this theoretical bound. As a result of the theory, the vulnerability of neural networks to small adversarial perturbations is a logical consequence of the amount of test error observed. We hope that our theoretical analysis of this very simple case will point the way forward to explore how the geometry of complex real-world data sets leads to adversarial examples. | Invite to Workshop Track |
ICLR.cc/2022/Conference | On the Convergence of mSGD and AdaGrad for Stochastic Optimization | As one of the most fundamental stochastic optimization algorithms, stochastic gradient descent (SGD) has been intensively developed and extensively applied in machine learning in the past decade. There have been some modified SGD-type algorithms, which outperform the SGD in many competitions and applications in terms of convergence rate and accuracy, such as momentum-based SGD (mSGD) and adaptive gradient algorithm (AdaGrad). Despite these empirical successes, the theoretical properties of these algorithms have not been well established due to technical difficulties. With this motivation, we focus on convergence analysis of mSGD and AdaGrad for any smooth (possibly non-convex) loss functions in stochastic optimization. First, we prove that the iterates of mSGD are asymptotically convergent to a connected set of stationary points with probability one, which is more general than existing works on subsequence convergence or convergence of time averages. Moreover, we prove that the loss function of mSGD decays at a certain rate faster than that of SGD. In addition, we prove the iterates of AdaGrad are asymptotically convergent to a connected set of stationary points with probability one. Also, this result extends the results from the literature on subsequence convergence and the convergence of time averages. Despite the generality of the above convergence results, we have relaxed some assumptions of gradient noises, convexity of loss functions, as well as boundedness of iterates. | Accept (Poster) |
ICLR.cc/2020/Conference | GraphSAINT: Graph Sampling Based Inductive Learning Method | Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs. To scale GCNs to large graphs, state-of-the-art methods use various layer sampling techniques to alleviate the "neighbor explosion" problem during minibatch training. We propose GraphSAINT, a graph sampling based inductive learning method that improves training efficiency and accuracy in a fundamentally different way. By changing perspective, GraphSAINT constructs minibatches by sampling the training graph, rather than the nodes or edges across GCN layers. Each iteration, a complete GCN is built from the properly sampled subgraph. Thus, we ensure fixed number of well-connected nodes in all layers. We further propose normalization technique to eliminate bias, and sampling algorithms for variance reduction. Importantly, we can decouple the sampling from the forward and backward propagation, and extend GraphSAINT with many architecture variants (e.g., graph attention, jumping connection). GraphSAINT demonstrates superior performance in both accuracy and training time on five large graphs, and achieves new state-of-the-art F1 scores for PPI (0.995) and Reddit (0.970). | Accept (Poster) |
ICLR.cc/2019/Conference | (Unconstrained) Beam Search is Sensitive to Large Search Discrepancies | Beam search is the most popular inference algorithm for decoding neural sequence models. Unlike greedy search, beam search allows for a non-greedy local decisions that can potentially lead to a sequence with a higher overall probability. However, previous work found that the performance of beam search tends to degrade with large beam widths. In this work, we perform an empirical study of the behavior of the beam search algorithm across three sequence synthesis tasks. We find that increasing the beam width leads to sequences that are disproportionately based on early and highly non-greedy decisions. These sequences typically include a very low probability token that is followed by a sequence of tokens with higher (conditional) probability leading to an overall higher probability sequence. However, as beam width increases, such sequences are more likely to have a lower evaluation score. Based on our empirical analysis we propose to constrain the beam search from taking highly non-greedy decisions early in the search. We evaluate two methods to constrain the search and show that constrained beam search effectively eliminates the problem of beam search degradation and in some cases even leads to higher evaluation scores. Our results generalize and improve upon previous observations on copies and training set predictions. | Reject |
ICLR.cc/2020/Conference | Dropout: Explicit Forms and Capacity Control | We investigate the capacity control provided by dropout in various machine learning problems. First, we study dropout for matrix sensing, where it induces a data-dependent regularizer that, in expectation, equals the weighted trace-norm of the product of the factors. In deep learning, we show that the data-dependent regularizer due to dropout directly controls the Rademacher complexity of the underlying class of deep neural networks. These developments enable us to give concrete generalization error bounds for the dropout algorithm in both matrix completion as well as training deep neural networks. We evaluate our theoretical findings on real-world datasets, including MovieLens, Fashion MNIST, and CIFAR-10. | Reject |
ICLR.cc/2022/Conference | Learning from One and Only One Shot | Humans can generalize from one or a few examples, and even from very little pre-training on similar tasks. Machine learning (ML) algorithms, however, typically require large data to either learn or pre-learn to transfer. Inspired by nativism, we directly model very basic human innate priors in abstract visual tasks like character or doodle recognition. The result is a white-box model that learns transformation-based topological similarity akin to how a human would naturally and unconsciously ``distort'' an object when first seeing it. Using the simple Nearest-Neighbor classifier in this similarity space, our model approaches human-level character recognition using only one to ten examples per class and nothing else (no pre-training). This is in contrast to one-shot and few-shot settings that require significant pre-training. On standard benchmarks including MNIST, EMNIST-letters, and the harder Omniglot challenge, our model outperforms both neural-network-based and classical ML methods in the ``tiny-data'' regime, including few-shot learning models that use an extra background set to perform transfer learning. Moreover, mimicking simple clustering methods like $k$-means but in a non-Euclidean space, our model can adapt to an unsupervised setting and generate human-interpretable archetypes of a class. | Reject |
ICLR.cc/2020/Conference | Self-Educated Language Agent with Hindsight Experience Replay for Instruction Following | Language creates a compact representation of the world and allows the description of unlimited situations and objectives through compositionality. These properties make it a natural fit to guide the training of interactive agents as it could ease recurrent challenges in Reinforcement Learning such as sample complexity, generalization, or multi-tasking. Yet, it remains an open-problem to relate language and RL in even simple instruction following scenarios. Current methods rely on expert demonstrations, auxiliary losses, or inductive biases in neural architectures. In this paper, we propose an orthogonal approach called Textual Hindsight Experience Replay (THER) that extends the Hindsight Experience Replay approach to the language setting. Whenever the agent does not fulfill its instruction, THER learn to output a new directive that matches the agent trajectory, and it relabels the episode with a positive reward. To do so, THER learns to map a state into an instruction by using past successful trajectories, which removes the need to have external expert interventions to relabel episodes as in vanilla HER. We observe that this simple idea also initiates a learning synergy between language acquisition and policy learning on instruction following tasks in the BabyAI environment. | Reject |
ICLR.cc/2023/Conference | UniMax: Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining | Pretrained multilingual large language models have typically used heuristic temperature-based sampling to balance between different languages. However previous work has not systematically evaluated the efficacy of different pretraining language distributions across model scales. In this paper, we propose a new sampling method, UniMax, that delivers more uniform coverage of head languages while mitigating overfitting on tail languages by explicitly capping the number of repeats over each language's corpus. We perform an extensive series of ablations testing a range of sampling strategies on a suite of multilingual benchmarks, while varying model scale. We find that UniMax outperforms standard temperature-based sampling, and the benefits persist as scale increases. As part of our contribution, we release: (i) an improved and refreshed mC4 multilingual corpus consisting of 29 trillion characters across 107 languages, and (ii) a suite of pretrained umT5 model checkpoints trained with UniMax sampling. | Accept: poster |
ICLR.cc/2018/Conference | DOUBLY STOCHASTIC ADVERSARIAL AUTOENCODER | Any autoencoder network can be turned into a generative model by imposing an arbitrary prior distribution on its hidden code vector. Variational Autoencoder uses a KL divergence penalty to impose the prior, whereas Adversarial Autoencoder uses generative adversarial networks. A straightforward modification of Adversarial Autoencoder can be achieved by replacing the adversarial network with maximum mean discrepancy (MMD) network. This replacement leads to a new set of probabilistic autoencoder which is also discussed in our paper.
However, an essential challenge remains in both of these probabilistic autoencoders, namely that the only source of randomness at the output of encoder, is the training data itself. Lack of enough stochasticity can make the optimization problem non-trivial. As a result, they can lead to degenerate solutions where the generator collapses into sampling only a few modes.
Our proposal is to replace the adversary of the adversarial autoencoder by a space of {\it stochastic} functions. This replacement introduces a a new source of randomness which can be considered as a continuous control for encouraging {\it explorations}. This prevents the adversary from fitting too closely to the generator and therefore leads to more diverse set of generated samples. Consequently, the decoder serves as a better generative network which unlike MMD nets scales linearly with the amount of data. We provide mathematical and empirical evidence on how this replacement outperforms the pre-existing architectures. | Reject |
ICLR.cc/2020/Conference | Understanding the (Un)interpretability of Natural Image Distributions Using Generative Models | Probability density estimation is a classical and well studied problem, but standard density estimation methods have historically lacked the power to model complex and high-dimensional image distributions. More recent generative models leverage the power of neural networks to implicitly learn and represent probability models over complex images. We describe methods to extract explicit probability density estimates from GANs, and explore the properties of these image density functions. We perform sanity check experiments to provide evidence that these probabilities are reasonable. However, we also show that density functions of natural images are difficult to interpret and thus limited in use. We study reasons for this lack of interpretability, and suggest that we can get better interpretability by doing density estimation on latent representations of images. | Withdrawn |
ICLR.cc/2023/Conference | CAN MACHINE TELL THE DISTORTION DIFFERENCE? A REVERSE ENGINEERING STUDY OF ADVERSARIAL ATTACKS | Deep neural networks have achieved remarkable performance in many areas, including image-related classification tasks. However, various studies have shown that they are vulnerable to adversarial examples – images that are carefully crafted to fool well-trained deep neural networks by introducing imperceptible perturbations to the original images. To better understand the inherent characteristics of adversarial attacks, we study the features of three common attack families: gradient-based, score-based, and decision-based. In this paper, we demonstrate that given adversarial examples, attacks from different families can be successfully identified with a simple model. To investigate the reason behind it, we further study the perturbation patterns of different attacks with carefully designed experiments. Experimental results on CIFAR10 and Tiny ImageNet confirm the differences of attacks in distortion patterns. | Desk_Rejected |
ICLR.cc/2020/Conference | Learning Time-Aware Assistance Functions for Numerical Fluid Solvers | Improving the accuracy of numerical methods remains a central challenge in many disciplines and is especially important for nonlinear simulation problems. A representative example of such problems is fluid flow, which has been thoroughly studied to arrive at efficient simulations of complex flow phenomena. This paper presents a data-driven approach that learns to improve the accuracy of numerical solvers. The proposed method utilizes an advanced numerical scheme with a fine simulation resolution to acquire reference data. We, then, employ a neural network that infers a correction to move a coarse thus quickly obtainable result closer to the reference data. We provide insights into the targeted learning problem with different learning approaches: fully supervised learning methods with a naive and an optimized data acquisition as well as an unsupervised learning method with a differentiable Navier-Stokes solver. While our approach is very general and applicable to arbitrary partial differential equation models, we specifically highlight gains in accuracy for fluid flow simulations. | Reject |
ICLR.cc/2022/Conference | Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities | How to learn an effective reinforcement learning-based model for control tasks from high-level visual observations is a practical and challenging problem. A key to solving this problem is to learn low-dimensional state representations from observations, from which an effective policy can be learned. In order to boost the learning of state encoding, recent works are focused on capturing behavioral similarities between state representations or applying data augmentation on visual observations. In this paper, we propose a novel meta-learner-based framework for representation learning regarding behavioral similarities for reinforcement learning. Specifically, our framework encodes the high-dimensional observations into two decomposed embeddings regarding reward and dynamics in a Markov Decision Process (MDP). A pair of meta-learners are developed, one of which quantifies the reward similarity and the other quantifies dynamics similarity over the correspondingly decomposed embeddings. The meta-learners are self-learned to update the state embeddings by approximating two disjoint terms in on-policy bisimulation metric. To incorporate the reward and dynamics terms, we further develop a strategy to adaptively balance their impacts based on different tasks or environments. We empirically demonstrate that our proposed framework outperforms state-of-the-art baselines on several benchmarks, including conventional DM Control Suite, Distracting DM Control Suite and a self-driving task CARLA. | Accept (Poster) |
ICLR.cc/2022/Conference | Latent Space Smoothing for Individually Fair Representations | Fair representation learning encodes user data to ensure fairness and utility, regardless of the downstream application. However, learning individually fair representations, i.e., guaranteeing that similar individuals are treated similarly, remains challenging in high-dimensional settings such as computer vision. In this work, we introduce LASSI, the first representation learning method for certifying individual fairness of high-dimensional data. Our key insight is to leverage recent advances in generative modeling to capture the set of similar individuals in the generative latent space. This allows learning an individually fair representation where similar individuals are mapped close together, by using adversarial training to minimize the distance between the representations of similar individuals. Finally, we employ randomized smoothing to provably map similar individuals close together, in turn ensuring that local robustness verification of the downstream application results in end-to-end fairness certification. Our experimental evaluation on challenging real-world image data demonstrates that our method increases certified individual fairness by more than 60%, without significantly affecting task utility. | Withdrawn |
ICLR.cc/2021/Conference | Efficient Conformal Prediction via Cascaded Inference with Expanded Admission | In this paper, we present a novel approach for conformal prediction (CP), in which we aim to identify a set of promising prediction candidates---in place of a single prediction. This set is guaranteed to contain a correct answer with high probability, and is well-suited for many open-ended classification tasks. In the standard CP paradigm, the predicted set can often be unusably large and also costly to obtain. This is particularly pervasive in settings where the correct answer is not unique, and the number of total possible answers is high. We first expand the CP correctness criterion to allow for additional, inferred "admissible" answers, which can substantially reduce the size of the predicted set while still providing valid performance guarantees. Second, we amortize costs by conformalizing prediction cascades, in which we aggressively prune implausible labels early on by using progressively stronger classifiers---again, while still providing valid performance guarantees. We demonstrate the empirical effectiveness of our approach for multiple applications in natural language processing and computational chemistry for drug discovery. | Accept (Poster) |
ICLR.cc/2020/Conference | REFINING MONTE CARLO TREE SEARCH AGENTS BY MONTE CARLO TREE SEARCH | Reinforcement learning methods that continuously learn neural networks by episode generation with game tree search have been successful in two-person complete information deterministic games such as chess, shogi, and Go. However, there are only reports of practical cases and there are little evidence to guarantee the stability and the final performance of learning process. In this research, the coordination of episode generation was focused on. By means of regarding the entire system as game tree search, the new method can handle the trade-off between exploitation and exploration during episode generation. The experiments with a small problem showed that it had robust performance compared to the existing method, Alpha Zero. | Reject |
ICLR.cc/2019/Conference | Excessive Invariance Causes Adversarial Vulnerability | Despite their impressive performance, deep neural networks exhibit striking failures on out-of-distribution inputs. One core idea of adversarial example research is to reveal neural network errors under such distribution shifts. We decompose these errors into two complementary sources: sensitivity and invariance. We show deep networks are not only too sensitive to task-irrelevant changes of their input, as is well-known from epsilon-adversarial examples, but are also too invariant to a wide range of task-relevant changes, thus making vast regions in input space vulnerable to adversarial attacks. We show such excessive invariance occurs across various tasks and architecture types. On MNIST and ImageNet one can manipulate the class-specific content of almost any image without changing the hidden activations. We identify an insufficiency of the standard cross-entropy loss as a reason for these failures. Further, we extend this objective based on an information-theoretic analysis so it encourages the model to consider all task-dependent features in its decision. This provides the first approach tailored explicitly to overcome excessive invariance and resulting vulnerabilities. | Accept (Poster) |
ICLR.cc/2022/Conference | On the Uncomputability of Partition Functions in Energy-Based Sequence Models | In this paper, we argue that energy-based sequence models backed by expressive parametric families can result in uncomputable and inapproximable partition functions. Among other things, this makes model selection--and therefore learning model parameters--not only difficult, but generally _undecidable_. The reason is that there are no good deterministic or randomized estimates of partition functions. Specifically, we exhibit a pathological example where under common assumptions, _no_ useful importance sampling estimates of the partition function can guarantee to have variance bounded below a rational number. As alternatives, we consider sequence model families whose partition functions are computable (if they exist), but at the cost of reduced expressiveness. Our theoretical results suggest that statistical procedures with asymptotic guarantees and sheer (but finite) amounts of compute are not the only things that make sequence modeling work; computability concerns must not be neglected as we consider more expressive model parametrizations. | Accept (Spotlight) |
ICLR.cc/2020/Conference | Gaussian Process Meta-Representations Of Neural Networks | Bayesian inference offers a theoretically grounded and general way to train neural networks and can potentially give calibrated uncertainty. It is, however, challenging to specify a meaningful and tractable prior over the network parameters. More crucially, many existing inference methods assume mean-field approximate posteriors, ignoring interactions between parameters in high-dimensional weight space. To this end, this paper introduces two innovations: (i) a Gaussian process-based hierarchical model for the network parameters based on recently introduced unit embeddings that can flexibly encode weight structures, and (ii) input-dependent contextual variables for the weight prior that can provide convenient ways to regularize the function space being modeled by the NN through the use of kernels.
Furthermore, we develop an efficient structured variational inference scheme that alleviates the need to perform inference in the weight space whilst retaining and learning non-trivial correlations between network parameters.
We show these models provide desirable test-time uncertainty estimates, demonstrate cases of modeling inductive biases for neural networks with kernels and demonstrate competitive predictive performance of the proposed model and algorithm over alternative approaches on a range of classification and active learning tasks. | Reject |
ICLR.cc/2019/Conference | Exploration in Policy Mirror Descent | Policy optimization is a core problem in reinforcement learning. In this paper, we investigate Reversed Entropy Policy Mirror Descent (REPMD), an on-line policy optimization strategy that improves exploration behavior while assuring monotonic progress in a principled objective. REPMD conducts a form of maximum entropy exploration within a mirror descent framework, but uses an alternative policy update with a reversed KL projection. This modified formulation bypasses undesirable mode seeking behavior and avoids premature convergence to sub-optimal policies, while still supporting strong theoretical properties such as guaranteed policy improvement. An experimental evaluation demonstrates that this approach significantly improves practical exploration and surpasses the empirical performance of state-of-the art policy optimization methods in a set of benchmark tasks. | Withdrawn |
ICLR.cc/2023/Conference | Scrunch: Preventing sensitive property inference through privacy-preserving representation learning | Many tasks that are commonly performed by devices attached to the Internet are currently being offloaded to the cloud, using the Machine Learning as a Service (MLaaS) paradigm. While this paradigm is motivated by the reduced capacity of mobile terminals, it also hinders privacy associated with the data exchanged over the network. Thus, the data exchanged among parties shall be conveniently anonymized to prevent possible confidentiality and privacy issues. While many privacy-enhancing algorithms have been proposed in the past, they are usually relying on very complex models that make difficult their applicability to real-world systems or envision too friendly attacker models. In this paper, we propose a deep learning system that creates anonymized representations for the data, while keeping the accuracy for the targeted MLaaS task high, assuming that the attacker can re-train an adversarial model. Our results show that the proposed algorithm i) is effective yet it uses a lighter approach than state-of-the-art ii) considers less friendly attacker models, and iii) outperforms the benchmark under different privacy metrics. | Reject |
ICLR.cc/2022/Conference | On the approximation properties of recurrent encoder-decoder architectures | Encoder-decoder architectures have recently gained popularity in sequence to sequence modelling, featuring in state-of-the-art models such as transformers. However, a mathematical understanding of their working principles still remains limited. In this paper, we study the approximation properties of recurrent encoder-decoder architectures. Prior work established theoretical results for RNNs in the linear setting, where approximation capabilities can be related to smoothness and memory of target temporal relationships. Here, we uncover that the encoder and decoder together form a particular “temporal product structure” which determines the approximation efficiency. Moreover, the encoder-decoder architecture generalises RNNs with the capability to learn time-inhomogeneous relationships. Our results provide the theoretical understanding of approximation properties of the recurrent encoder-decoder architecture, which precisely characterises, in the considered setting, the types of temporal relationships that can be efficiently learned. | Accept (Spotlight) |
ICLR.cc/2021/Conference | Automated Concatenation of Embeddings for Structured Prediction | Pretrained contextualized embeddings are powerful word representations for structured prediction tasks. Recent work found that better word representations can be obtained by concatenating different types of embeddings. However, the selection of embeddings to form the best concatenated representation usually varies depending on the task and the collection of candidate embeddings, and the ever-increasing number of embedding types makes it a more difficult problem. In this paper, we propose Automated Concatenation of Embeddings (ACE) to automate the process of finding better concatenations of embeddings for structured prediction tasks, based on a formulation inspired by recent progress on neural architecture search. Specifically, a controller alternately samples a concatenation of embeddings, according to its current belief of the effectiveness of individual embedding types in consideration for a task, and updates the belief based on a reward. We follow strategies in reinforcement learning to optimize the parameters of the controller and compute the reward based on the accuracy of a task model, which is fed with the sampled concatenation as input and trained on a task dataset. Empirical results on 6 tasks and 21 datasets show that our approach outperforms strong baselines and achieves state-of-the-art performance with fine-tuned embeddings in the vast majority of evaluations. | Reject |
ICLR.cc/2018/Conference | Autonomous Vehicle Fleet Coordination With Deep Reinforcement Learning | Autonomous vehicles are becoming more common in city transportation. Companies will begin to find a need to teach these vehicles smart city fleet coordination. Currently, simulation based modeling along with hand coded rules dictate the decision making of these autonomous vehicles. We believe that complex intelligent behavior can be learned by these agents through Reinforcement Learning.In this paper, we discuss our work for solving this system by adapting the Deep Q-Learning (DQN) model to the multi-agent setting. Our approach applies deep reinforcement learning by combining convolutional neural networks with DQN to teach agents to fulfill customer demand in an environment that is partially observ-able to them. We also demonstrate how to utilize transfer learning to teach agents to balance multiple objectives such as navigating to a charging station when its en-ergy level is low. The two evaluations presented show that our solution has shown hat we are successfully able to teach agents cooperation policies while balancing multiple objectives. | Reject |
ICLR.cc/2020/Conference | Probing Emergent Semantics in Predictive Agents via Question Answering | Recent work has demonstrated how predictive modeling can endow agents with rich knowledge of their surroundings, improving their ability to act in complex environments. We propose question-answering as a general paradigm to decode and understand the representations that such agents develop, applying our method to two recent approaches to predictive modeling – action-conditional CPC (Guo et al., 2018) and SimCore (Gregor et al., 2019). After training agents with these predictive objectives in a visually-rich, 3D environment with an assortment of objects, colors, shapes, and spatial configurations, we probe their internal state representations with a host of synthetic (English) questions, without backpropagating gradients from the question-answering decoder into the agent. The performance of different agents when probed in this way reveals that they learn to encode detailed, and seemingly compositional, information about objects, properties and spatial relations from their physical environment. Our approach is intuitive, i.e. humans can easily interpret the responses of the model as opposed to inspecting continuous vectors, and model-agnostic, i.e. applicable to any modeling approach. By revealing the implicit knowledge of objects, quantities, properties and relations acquired by agents as they learn, question-conditional agent probing can stimulate the design and development of stronger predictive learning objectives. | Reject |
ICLR.cc/2020/Conference | ProxNet: End-to-End Learning of Structured Representation by Proximal Mapping | Underpinning the success of deep learning is the effective regularization that allows a broad range of structures in data to be compactly modeled in a deep architecture. Examples include transformation invariances, robustness to adversarial/random perturbations, and correlations between multiple modalities. However, most existing methods incorporate such priors either by auto-encoders, whose result is used to initialize supervised learning, or by augmenting the data with exemplifications of the transformations which, despite the improved performance of supervised learning, leaves it unclear whether the learned latent representation does encode the desired regularities. To address these issues, this work proposes an \emph{end-to-end} representation learning framework that allows prior structures to be encoded \emph{explicitly} in the hidden layers, and to be trained efficiently in conjunction with the supervised target. Our approach is based on proximal mapping in a reproducing kernel Hilbert space, and leverages differentiable optimization. The resulting technique is applied to generalize dropout and invariant kernel warping, and to develop novel algorithms for multiview modeling and robust temporal learning. | Withdrawn |
ICLR.cc/2020/Conference | Anchor & Transform: Learning Sparse Representations of Discrete Objects | Learning continuous representations of discrete objects such as text, users, and items lies at the heart of many applications including text and user modeling. Unfortunately, traditional methods that embed all objects do not scale to large vocabulary sizes and embedding dimensions. In this paper, we propose a general method, Anchor & Transform (ANT) that learns sparse representations of discrete objects by jointly learning a small set of anchor embeddings and a sparse transformation from anchor objects to all objects. ANT is scalable, flexible, end-to-end trainable, and allows the user to easily incorporate domain knowledge about object relationships (e.g. WordNet, co-occurrence, item clusters). ANT also recovers several task-specific baselines under certain structural assumptions on the anchors and transformation matrices. On text classification and language modeling benchmarks, ANT demonstrates stronger performance with fewer parameters as compared to existing vocabulary selection and embedding compression baselines. | Reject |
ICLR.cc/2018/Conference | Non-Autoregressive Neural Machine Translation | Existing approaches to neural machine translation condition each output word on previously generated outputs. We introduce a model that avoids this autoregressive property and produces its outputs in parallel, allowing an order of magnitude lower latency during inference. Through knowledge distillation, the use of input token fertilities as a latent variable, and policy gradient fine-tuning, we achieve this at a cost of as little as 2.0 BLEU points relative to the autoregressive Transformer network used as a teacher. We demonstrate substantial cumulative improvements associated with each of the three aspects of our training strategy, and validate our approach on IWSLT 2016 English–German and two WMT language pairs. By sampling fertilities in parallel at inference time, our non-autoregressive model achieves near-state-of-the-art performance of 29.8 BLEU on WMT 2016 English–Romanian. | Accept (Poster) |
ICLR.cc/2020/Conference | Stochastic Mirror Descent on Overparameterized Nonlinear Models | Most modern learning problems are highly overparameterized, meaning that the model has many more parameters than the number of training data points, and as a result, the training loss may have infinitely many global minima (in fact, a manifold of parameter vectors that perfectly interpolates the training data). Therefore, it is important to understand which interpolating solutions we converge to, how they depend on the initialization point and the learning algorithm, and whether they lead to different generalization performances. In this paper, we study these questions for the family of stochastic mirror descent (SMD) algorithms, of which the popular stochastic gradient descent (SGD) is a special case. Recently it has been shown that, for overparameterized linear models, SMD converges to the global minimum that is closest (in terms of the Bregman divergence of the mirror used) to the initialization point, a phenomenon referred to as implicit regularization. Our contributions in this paper are both theoretical and experimental. On the theory side, we show that in the overparameterized nonlinear setting, if the initialization is close enough to the manifold of global optima, SMD with sufficiently small step size converges to a global minimum that is approximately the closest global minimum in Bregman divergence, thus attaining approximate implicit regularization. For highly overparametrized models, this closeness comes for free: the manifold of global optima is so high dimensional that with high probability an arbitrarily chosen initialization will be close to the manifold. On the experimental side, our extensive experiments on the MNIST and CIFAR-10 datasets, using various initializations, various mirror descents, and various Bregman divergences, consistently confirms that this phenomenon indeed happens in deep learning: SMD converges to the closest global optimum to the initialization point in the Bregman divergence of the mirror used. Our experiments further indicate that there is a clear difference in the generalization performance of the solutions obtained from different SMD algorithms. Experimenting on the CIFAR-10 dataset with different regularizers, l1 to encourage sparsity, l2 (yielding SGD) to encourage small Euclidean norm, and l10 to discourage large components in the parameter vector, consistently and definitively shows that, for small initialization vectors, l10-SMD has better generalization performance than SGD, which in turn has better generalization performance than l1-SMD. This surprising, and perhaps counter-intuitive, result strongly suggests the importance of a comprehensive study of the role of regularization, and the choice of the best regularizer, to improve the generalization performance of deep networks. | Reject |
ICLR.cc/2023/Conference | Emergent collective intelligence from massive-agent cooperation and competition | Inspired by organisms evolving through cooperation and competition between different populations on Earth, we study the emergence of artificial collective intelligence through massive-agent reinforcement learning. To this end, We propose a new massive-agent reinforcement learning environment, Lux, where dynamic and massive agents in two teams scramble for limited resources and fight off the darkness. In Lux, we build our agents through the standard reinforcement learning algorithm in curriculum learning phases and leverage centralized control via a pixel-to-pixel policy network. As agents co-evolve through self-play, we observe several stages of intelligence, from the acquisition of atomic skills to the development of group strategies. Since these learned group strategies arise from individual decisions without an explicit coordination mechanism, we claim that artificial collective intelligence emerges from massive-agent cooperation and competition. We further analyze the emergence of various learned strategies through metrics and ablation studies, aiming to provide insights for reinforcement learning implementations in massive-agent environments. | Reject |
ICLR.cc/2021/Conference | Adaptive Gradient Methods Can Be Provably Faster than SGD with Random Shuffling | Adaptive gradient methods have been shown to outperform SGD in many tasks of training neural networks. However, the acceleration effect is yet to be explained in the non-convex setting since the best convergence rate of adaptive gradient methods is worse than that of SGD in literature. In this paper, we prove that adaptive gradient methods exhibit an $\small\tilde{O}(T^{-1/2})$-convergence rate for finding first-order stationary points under the strong growth condition, which improves previous best convergence results of adaptive gradient methods and random shuffling SGD by factors of $\small O(T^{-1/4})$ and $\small O(T^{-1/6})$, respectively. In particular, we study two variants of AdaGrad with random shuffling for finite sum minimization. Our analysis suggests that the combination of random shuffling and adaptive learning rates gives rise to better convergence. | Reject |
ICLR.cc/2022/Conference | Selective Cross-Domain Consistency Regularization for Time Series Domain Generalization | Domain generalization aims to learn models robust to domain shift, with limited source domains at training and without any access to target domain samples except at test time. Current domain alignment methods seek to extract features invariant across all domains, but do not consider inter-domain relationships. In this paper, we propose a novel representation learning methodology for time series classification that selectively enforces prediction consistency between source domains estimated to be closely-related. Specifically, we view a domain shift as a form of data transformation that preserves labels but not necessarily class relationships, and we regularize the predicted class relationships to be shared only by closely-related domains instead of all domains to prevent negative transfer. We conduct comprehensive experiments on two public real-world datasets. The proposed method significantly improves over the baseline and achieves better or competitive performance in comparison with state-of-the-art methods. | Withdrawn |
ICLR.cc/2020/Conference | Learning to Contextually Aggregate Multi-Source Supervision for Sequence Labeling | Sequence labeling is a fundamental framework for various natural language processing problems including part-of-speech tagging and named entity recognition. Its performance is largely influenced by the annotation quality and quantity in supervised learning scenarios. In many cases, ground truth labels are costly and time-consuming to collect or even non-existent, while imperfect ones could be easily accessed or transferred from different domains. A typical example is crowd-sourced datasets which have multiple annotations for each sentence which may be noisy or incomplete. Additionally, predictions from multiple source models in transfer learning can be seen as a case of multi-source supervision. In this paper, we propose a novel framework named Consensus Network (CONNET) to conduct training with imperfect annotations from multiple sources. It learns the representation for every weak supervision source and dynamically aggregates them by a context-aware attention mechanism. Finally, it leads to a model reflecting the consensus among multiple sources. We evaluate the proposed framework in two practical settings of multi-source learning: learning with crowd annotations and unsupervised cross-domain model adaptation. Extensive experimental results show that our model achieves significant improvements over existing methods in both settings. | Reject |
ICLR.cc/2018/Conference | Learning Latent Representations in Neural Networks for Clustering through Pseudo Supervision and Graph-based Activity Regularization | In this paper, we propose a novel unsupervised clustering approach exploiting the hidden information that is indirectly introduced through a pseudo classification objective. Specifically, we randomly assign a pseudo parent-class label to each observation which is then modified by applying the domain specific transformation associated with the assigned label. Generated pseudo observation-label pairs are subsequently used to train a neural network with Auto-clustering Output Layer (ACOL) that introduces multiple softmax nodes for each pseudo parent-class. Due to the unsupervised objective based on Graph-based Activity Regularization (GAR) terms, softmax duplicates of each parent-class are specialized as the hidden information captured through the help of domain specific transformations is propagated during training. Ultimately we obtain a k-means friendly latent representation. Furthermore, we demonstrate how the chosen transformation type impacts performance and helps propagate the latent information that is useful in revealing unknown clusters. Our results show state-of-the-art performance for unsupervised clustering tasks on MNIST, SVHN and USPS datasets, with the highest accuracies reported to date in the literature. | Accept (Poster) |
ICLR.cc/2022/Conference | Unsupervised Domain Adaptation By Optimal Transportation Of Clusters Between Domains | Unsupervised domain adaptation (UDA) aims to transfer the knowledge from a labeled source domain to an unlabeled target domain. Typically, to guarantee desirable knowledge transfer, aligning the distribution between source and target domain from a global perspective is widely adopted in UDA. Recent researchers further point out the importance of local-level alignment and borrow the experience from Optimal Transport (OT) theory to construct instance-pair alignment.
However, existing OT-based algorithms are limited to resolve class imbalance challenge and require a huge computation cost when considering a large-scale training situation.
In this paper, we address these two issues by proposing a Clustering-based Optimal Transport (COT) algorithm, which formulates the alignment procedure as an Optimal Transport problem by capturing the fine-grained attribute alignment. Concretely, COT innovatively designs the loss derived from discrete Kantorovich dual form to construct a mapping between clustering centers in source and target domain, which simultaneously eliminates the negative effect brought by class imbalance and reduces the computation cost on the basis of theoretical analysis. Finally, our COT together with some previous UDA methods achieve superior performance on several benchmarks. | Withdrawn |
ICLR.cc/2019/Conference | Efficient Augmentation via Data Subsampling | Data augmentation is commonly used to encode invariances in learning methods. However, this process is often performed in an inefficient manner, as artificial examples are created by applying a number of transformations to all points in the training set. The resulting explosion of the dataset size can be an issue in terms of storage and training costs, as well as in selecting and tuning the optimal set of transformations to apply. In this work, we demonstrate that it is possible to significantly reduce the number of data points included in data augmentation while realizing the same accuracy and invariance benefits of augmenting the entire dataset. We propose a novel set of subsampling policies, based on model influence and loss, that can achieve a 90% reduction in augmentation set size while maintaining the accuracy gains of standard data augmentation. | Accept (Poster) |
ICLR.cc/2021/Conference | PODS: Policy Optimization via Differentiable Simulation | Current reinforcement learning (RL) methods use simulation models as simple black-box oracles. In this paper, with the goal of improving the performance exhibited by RL algorithms, we explore a systematic way of leveraging the additional information provided by an emerging class of differentiable simulators. Building on concepts established by Deterministic Policy Gradients (DPG) methods, the neural network policies learned with our approach represent deterministic actions. In a departure from standard methodologies, however, learning these policy does not hinge on approximations of the value function that must be learned concurrently in an actor-critic fashion. Instead, we exploit differentiable simulators to directly compute the analytic gradient of a policy's value function with respect to the actions it outputs. This, in turn, allows us to efficiently perform locally optimal policy improvement iterations. Compared against other state-of-the-art RL methods, we show that with minimal hyper-parameter tuning our approach consistently leads to better asymptotic behavior across a set of payload manipulation tasks that demand high precision. | Reject |
ICLR.cc/2020/Conference | Topology of deep neural networks | We study how the topology of a data set comprising two components representing two classes of objects in a binary classification problem changes as it passes through the layers of a well-trained neural network, i.e., one with perfect accuracy on training set and a generalization error of less than 1%. The goal is to shed light on two well-known mysteries in deep neural networks: (i) a nonsmooth activation function like ReLU outperforms a smooth one like hyperbolic tangent; (ii) successful neural network architectures rely on having many layers, despite the fact that a shallow network is able to approximate any function arbitrary well. We performed extensive experiments on persistent homology of a range of point cloud data sets. The results consistently demonstrate the following: (1) Neural networks operate by changing topology, transforming a topologically complicated data set into a topologically simple one as it passes through the layers. No matter how complicated the topology of the data set we begin with, when passed through a well-trained neural network, the Betti numbers of both components invariably reduce to their lowest possible values: zeroth Betti number is one and all higher Betti numbers are zero. Furthermore, (2) the reduction in Betti numbers is significantly faster for ReLU activation compared to hyperbolic tangent activation --- consistent with the fact that the former define nonhomeomorphic maps (that change topology) whereas the latter define homeomorphic maps (that preserve topology). Lastly, (3) shallow and deep networks process the same data set differently --- a shallow network operates mainly through changing geometry and changes topology only in its final layers, a deep network spreads topological changes more evenly across all its layers. | Withdrawn |
ICLR.cc/2020/Conference | Online and stochastic optimization beyond Lipschitz continuity: A Riemannian approach | Motivated by applications to machine learning and imaging science, we study a class of online and stochastic optimization problems with loss functions that are not Lipschitz continuous; in particular, the loss functions encountered by the optimizer could exhibit gradient singularities or be singular themselves. Drawing on tools and techniques from Riemannian geometry, we examine a Riemann–Lipschitz (RL) continuity condition which is tailored to the singularity landscape of the problem’s loss functions. In this way, we are able to tackle cases beyond the Lipschitz framework provided by a global norm, and we derive optimal regret bounds and last iterate convergence results through the use of regularized learning methods (such as online mirror descent). These results are subsequently validated in a class of stochastic Poisson inverse problems that arise in imaging science. | Accept (Spotlight) |
ICLR.cc/2022/Conference | Automatic Integration for Neural Temporal Point Process | Integration lies at the heart of the temporal point process. Due to the intrinsic mathematical difficulty of symbolic integration, neural temporal point process models either constrain the intensity function to an integrable functional form or apply certain numerical methods. However, the former type of model has limited expressive power, and the latter type of model suffers additional numerical errors and high computational costs. In this paper, we introduce automatic integration with neural point process models, a new paradigm for efficient, closed-form nonparametric inference of temporal point process characterized by any intensity function. We test the model against a variety of synthetic temporal point process datasets and show that the model can better capture inter-event intensity changes than state-of-the-art methods. We also identify certain model settings that would lead the MLE estimator for the temporal point process to be inconsistent. | Withdrawn |
ICLR.cc/2023/Conference | Boosting Drug-Target Affinity Prediction from Nearest Neighbors | Precisely predicting Drug-Target binding Affinity (DTA) is essential for drug discovery.
Recently, deep learning methods have been popular with DTA prediction. However, the prediction accuracy is still far from satisfaction.
In this work, inspired by the recent success of retrieval methods, we propose $k$NN-DTA, a non-parametric embedding-based retrieval method adopted on a pre-trained DTA prediction model, which can extend the power of the neural DTA model with no or negligible cost.
Compared to traditional chemical similarity retrieval, our embedding-based retrieval shows extremely high efficiency.
Different from existing methods, we introduce two neighbor aggregation ways from both embedding space and label space that are integrated in a unified framework.
Specifically, we propose a \emph{label aggregation} with \emph{pair-wise retrieval} and a \emph{representation aggregation} with \emph{point-wise retrieval} of the nearest neighbors.
This method executes in the inference phase and can efficiently boost the DTA prediction performance with no training cost.
In addition, we propose an extension, Ada-$k$NN-DTA, an instance-wise and adaptive aggregation with lightweight learning.
Results on four benchmark datasets show that $k$NN-DTA brings significant improvements, outperforming previous state-of-the-art (SOTA) results, e.g, on BindingDB IC$_{50}$ and $K_i$ testbeds, $k$NN-DTA obtains new records of RMSE scores $\bf{0.687}$ and $\bf{0.748}$ with both $\bf{4}$ point improvement.
The extended Ada-$k$NN-DTA can further improve the performance, e.g., another $\bf{1}$ point gain on BindingDB. These results strongly prove the effectiveness and efficiency of our method.
Results on other settings and comprehensive studies/analyses also show the great potential of our $k$NN-DTA approach. | Withdrawn |
ICLR.cc/2023/Conference | DREAM: Domain-free Reverse Engineering Attributes of Black-box Model | Deep learning models are usually black boxes when deployed on machine learning platforms. Prior works have shown that the attributes (e.g., the number of convolutional layers) of a target black-box neural network can be exposed through a sequence of queries. There is a crucial limitation that these works assume the dataset used for training the target model to be known beforehand, and leverage this dataset for model attribute attack. However, it is difficult to access the training dataset of the target black-box model in reality. Therefore, whether the attributes of a target black-box model could be still revealed in this case is doubtful. In this paper, we investigate a new problem of Domain-free Reverse Engineering the Attributes of a black-box target Model, called DREAM, without requiring the model's training dataset available, and put forward a general and principled framework by casting this problem as an out of distribution (OOD) generalization problem. At the heart of our framework, we devise a multi-discriminator generative adversarial network (MDGAN) to learn domain invariant features. Based on these features, we can learn a domain-free model to inversely infer the attributes of a target black-box model with unknown training data. This makes our method one of the kinds that can gracefully apply to an arbitrary domain for model attribute reverse engineering with good generalization ability. Extensive experimental studies are conducted and the results validate the superiority of our proposed method over the baselines. | Reject |
ICLR.cc/2023/Conference | HierBatching: Locality-Aware Out-of-Core Training of Graph Neural Networks | As graph neural networks (GNNs) become increasingly more popular for analyzing data organized as massive graphs, how these models can be efficiently trained under economic computing resources becomes a critical subject that influences the widespread adoption of GNNs in practice. We consider the use of a single commodity machine restrained by limited memory but otherwise is attached with ample external storage. In such an under-explored scenario, not only the feature data often exceeds the memory capacity, but also the graph structure may not fit in memory as well. Then, with data stored on disk, gathering features and constructing neighborhood subgraphs in a usual mini-batch training incur inefficient random access and expensive data movement.
To overcome this bottleneck, we propose a locality-aware training scheme, coined HierBatching, to significantly increase sequential disk access, while maintaining the random nature of stochastic training and its quality. HierBatching exploits the memory hierarchy of a modern GPU machine and constructs batches in an analogously hierarchical manner. Therein, graph nodes are organized in many partitions, each of which is laid out contiguously in disk for maximal spatial locality; while the main memory stores random partitions and is treated as the cache of the disk. Its content is reused multiple times for improving temporal locality. We conduct comprehensive experiments, including locality ablation, to demonstrate that HierBatching is economic, fast, and accurate. | Reject |
ICLR.cc/2021/Conference | Learning Parametrised Graph Shift Operators | In many domains data is currently represented as graphs and therefore, the graph representation of this data becomes increasingly important in machine learning. Network data is, implicitly or explicitly, always represented using a graph shift operator (GSO) with the most common choices being the adjacency, Laplacian matrices and their normalisations. In this paper, a novel parametrised GSO (PGSO) is proposed, where specific parameter values result in the most commonly used GSOs and message-passing operators in graph neural network (GNN) frameworks. The PGSO is suggested as a replacement of the standard GSOs that are used in state-of-the-art GNN architectures and the optimisation of the PGSO parameters is seamlessly included in the model training. It is proved that the PGSO has real eigenvalues and a set of real eigenvectors independent of the parameter values and spectral bounds on the PGSO are derived. PGSO parameters are shown to adapt to the sparsity of the graph structure in a study on stochastic blockmodel networks, where they are found to automatically replicate the GSO regularisation found in the literature. On several real-world datasets the accuracy of state-of-the-art GNN architectures is improved by the inclusion of the PGSO in both node- and graph-classification tasks. | Accept (Poster) |
ICLR.cc/2021/Conference | Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge | Selecting causal inference models for estimating individualized treatment effects (ITE) from observational data presents a unique challenge since the counterfactual outcomes are never observed. The problem is challenged further in the unsupervised domain adaptation (UDA) setting where we only have access to labeled samples in the source domain, but desire selecting a model that achieves good performance on a target domain for which only unlabeled samples are available. Existing techniques for UDA model selection are designed for the predictive setting. These methods examine discriminative density ratios between the input covariates in the source and target domain and do not factor in the model's predictions in the target domain. Because of this, two models with identical performance on the source domain would receive the same risk score by existing methods, but in reality, have significantly different performance in the test domain. We leverage the invariance of causal structures across domains to propose a novel model selection metric specifically designed for ITE methods under the UDA setting. In particular, we propose selecting models whose predictions of interventions' effects satisfy known causal structures in the target domain. Experimentally, our method selects ITE models that are more robust to covariate shifts on several healthcare datasets, including estimating the effect of ventilation in COVID-19 patients from different geographic locations. | Reject |
ICLR.cc/2018/Conference | Minimax Curriculum Learning: Machine Teaching with Desirable Difficulties and Scheduled Diversity | We introduce and study minimax curriculum learning (MCL), a new method for adaptively selecting a sequence of training subsets for a succession of stages in machine learning. The subsets are encouraged to be small and diverse early on, and then larger, harder, and allowably more homogeneous in later stages. At each stage, model weights and training sets are chosen by solving a joint continuous-discrete minimax optimization, whose objective is composed of a continuous loss (reflecting training set hardness) and a discrete submodular promoter of diversity for the chosen subset. MCL repeatedly solves a sequence of such optimizations with a schedule of increasing training set size and decreasing pressure on diversity encouragement. We reduce MCL to the minimization of a surrogate function handled by submodular maximization and continuous gradient methods. We show that MCL achieves better performance and, with a clustering trick, uses fewer labeled samples for both shallow and deep models while achieving the same performance. Our method involves repeatedly solving constrained submodular maximization of an only slowly varying function on the same ground set. Therefore, we develop a heuristic method that utilizes the previous submodular maximization solution as a warm start for the current submodular maximization process to reduce computation while still yielding a guarantee. | Accept (Poster) |
ICLR.cc/2019/Conference | Hierarchical interpretations for neural network predictions | Deep neural networks (DNNs) have achieved impressive predictive performance due to their ability to learn complex, non-linear relationships between variables. However, the inability to effectively visualize these relationships has led to DNNs being characterized as black boxes and consequently limited their applications. To ameliorate this problem, we introduce the use of hierarchical interpretations to explain DNN predictions through our proposed method: agglomerative contextual decomposition (ACD). Given a prediction from a trained DNN, ACD produces a hierarchical clustering of the input features, along with the contribution of each cluster to the final prediction. This hierarchy is optimized to identify clusters of features that the DNN learned are predictive. We introduce ACD using examples from Stanford Sentiment Treebank and ImageNet, in order to diagnose incorrect predictions, identify dataset bias, and extract polarizing phrases of varying lengths. Through human experiments, we demonstrate that ACD enables users both to identify the more accurate of two DNNs and to better trust a DNN's outputs. We also find that ACD's hierarchy is largely robust to adversarial perturbations, implying that it captures fundamental aspects of the input and ignores spurious noise. | Accept (Poster) |
ICLR.cc/2018/Conference | Long-term Forecasting using Tensor-Train RNNs | We present Tensor-Train RNN (TT-RNN), a novel family of neural sequence architectures for multivariate forecasting in environments with nonlinear dynamics. Long-term forecasting in such systems is highly challenging, since there exist long-term temporal dependencies, higher-order correlations and sensitivity to error propagation. Our proposed tensor recurrent architecture addresses these issues by learning the nonlinear dynamics directly using higher order moments and high-order state transition functions. Furthermore, we decompose the higher-order structure using the tensor-train (TT) decomposition to reduce the number of parameters while preserving the model performance. We theoretically establish the approximation properties of Tensor-Train RNNs for general sequence inputs, and such guarantees are not available for usual RNNs. We also demonstrate significant long-term prediction improvements over general RNN and LSTM architectures on a range of simulated environments with nonlinear dynamics, as well on real-world climate and traffic data. | Reject |
ICLR.cc/2020/Conference | Improving the Generalization of Visual Navigation Policies using Invariance Regularization | Training agents to operate in one environment often yields overfitted models that are unable to generalize to the changes in that environment. However, due to the numerous variations that can occur in the real-world, the agent is often required to be robust in order to be useful. This has not been the case for agents trained with reinforcement learning (RL) algorithms. In this paper, we investigate the overfitting of RL agents to the training environments in visual navigation tasks. Our experiments show that deep RL agents can overfit even when trained on multiple environments simultaneously.
We propose a regularization method which combines RL with supervised learning methods by adding a term to the RL objective that would encourage the invariance of a policy to variations in the observations that ought not to affect the action taken. The results of this method, called invariance regularization, show an improvement in the generalization of policies to environments not seen during training.
| Reject |
ICLR.cc/2023/Conference | Modelling Long Range Dependencies in $N$D: From Task-Specific to a General Purpose CNN | Performant Convolutional Neural Network (CNN) architectures must be tailored to specific tasks in order to consider the length, resolution, and dimensionality of the input data. In this work, we tackle the need for problem-specific CNN architectures. We present the Continuous Convolutional Neural Network (CCNN): a single CNN able to process data of arbitrary resolution, dimensionality and length without any structural changes. Its key component are its continuous convolutional kernels which model long-range dependencies at every layer, and thus remove the need of current CNN architectures for task-dependent downsampling and depths. We showcase the generality of our method by using the same architecture for tasks on sequential ($1{\rm D}$), visual ($2{\rm D}$) and point-cloud ($3{\rm D}$) data. Our CCNN matches and often outperforms the current state-of-the-art across all tasks considered. | Accept: poster |
ICLR.cc/2020/Conference | Towards More Realistic Neural Network Uncertainties | Statistical models are inherently uncertain. Quantifying or at least upper-bounding their uncertainties is vital for safety-critical systems. While standard neural networks do not report this information, several approaches exist to integrate uncertainty estimates into them. Assessing the quality of these uncertainty estimates is not straightforward, as no direct ground truth labels are available. Instead, implicit statistical assessments are required. For regression, we propose to evaluate uncertainty realism---a strict quality criterion---with a Mahalanobis distance-based statistical test. An empirical evaluation reveals the need for uncertainty measures that are appropriate to upper-bound heavy-tailed empirical errors. Alongside, we transfer the variational U-Net classification architecture to standard supervised image-to-image tasks. It provides two uncertainty mechanisms and significantly improves uncertainty realism compared to a plain encoder-decoder model. | Reject |
ICLR.cc/2020/Conference | Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation | Achieving faster execution with shorter compilation time can foster further diversity and innovation in neural networks. However, the current paradigm of executing neural networks either relies on hand-optimized libraries, traditional compilation heuristics, or very recently genetic algorithms and other stochastic methods. These methods suffer from frequent costly hardware measurements rendering them not only too time consuming but also suboptimal. As such, we devise a solution that can learn to quickly adapt to a previously unseen design space for code optimization, both accelerating the search and improving the output performance. This solution dubbed Chameleon leverages reinforcement learning whose solution takes fewer steps to converge, and develops an adaptive sampling algorithm that not only focuses on the costly samples (real hardware measurements) on representative points but also uses a domain-knowledge inspired logic to improve the samples itself. Experimentation with real hardware shows that Chameleon provides 4.45x speed up in optimization time over AutoTVM, while also improving inference time of the modern deep networks by 5.6%. | Accept (Poster) |
ICLR.cc/2023/Conference | Exploring Generalization of Non-Contrastive self-supervised Learning | Contrastive learning have recently produced results comparable to the state-of-the-art supervised models. Non-contrastive methods do not use negative samples, but separate samples of different classes by explicitly or implicitly optimizing the representation space. Although we have some understanding of the core of the
non-contrastive learning method, theoretical analysis of its generalization performance is still missing. Thus we present a theoretical analysis of generalizability of non-contrastive models. We focus on the inter-class distance, show how non-contrastive methods increase the inter-class distance, and how the distance affects the
generalization performance of the model. We find that the generalization of non-contrastive methods is affected by the output dimension and the number of latent classes. Models with much fewer dimensions than the number of latent classes are not sufficient to generalize well. We demonstrate our findings through experiments on the CIFAR dataset. | Withdrawn |
ICLR.cc/2023/Conference | Impact of the Last Fully Connected Layer on Out-of-distribution Detection | Out-of-distribution (OOD) detection, a task that aims to detect OOD data during deployment, has received lots of research attention recently, due to its importance for the safe deployment of deep models. In this task, a major problem is how to handle the overconfidence problem in OOD data. While this problem has been explored from several perspectives in previous works, such as the measure of OOD uncertainty and the activation function, the connection between the last fully connected (FC) layer and this overconfidence problem is still less explored. In this paper, we find that the weight of the last FC layer of the model trained on in-distribution (ID) data can be an important source of the overconfidence problem, and we propose a simple yet effective OOD detection method to assign the weight of the last FC layer with small values instead of using the original weight trained on ID data. We analyze in Sec.5 that our proposed method can make the OOD data and the ID data to be more separable, and thus alleviate the overconfidence problem. Moreover, our proposed method can be flexibly applied on various off-the-shelf OOD detection methods. We show the effectiveness of our proposed method through extensive experiments on the ImageNet dataset, the CIFAR-10 dataset, and the CIFAR-100 dataset. | Withdrawn |
ICLR.cc/2019/Conference | Lipschitz regularized Deep Neural Networks generalize | We show that if the usual training loss is augmented by a Lipschitz regularization term, then the networks generalize. We prove generalization by first establishing a stronger convergence result, along with a rate of convergence. A second result resolves a question posed in Zhang et al. (2016): how can a model distinguish between the case of clean labels, and randomized labels? Our answer is that Lipschitz regularization using the Lipschitz constant of the clean data makes this distinction. In this case, the model learns a different function which we hypothesize correctly fails to learn the dirty labels. | Reject |
ICLR.cc/2020/Conference | Unsupervised Meta-Learning for Reinforcement Learning | Meta-learning algorithms learn to acquire new tasks more quickly from past experience. In the context of reinforcement learning, meta-learning algorithms can acquire reinforcement learning procedures to solve new problems more efficiently by utilizing experience from prior tasks. The performance of meta-learning algorithms depends on the tasks available for meta-training: in the same way that supervised learning generalizes best to test points drawn from the same distribution as the training points, meta-learning methods generalize best to tasks from the same distribution as the meta-training tasks. In effect, meta-reinforcement learning offloads the design burden from algorithm design to task design. If we can automate the process of task design as well, we can devise a meta-learning algorithm that is truly automated. In this work, we take a step in this direction, proposing a family of unsupervised meta-learning algorithms for reinforcement learning. We motivate and describe a general recipe for unsupervised meta-reinforcement learning, and present an instantiation of this approach. Our conceptual and theoretical contributions consist of formulating the unsupervised meta-reinforcement learning problem and describing how task proposals based on mutual information can in principle be used to train optimal meta-learners. Our experimental results indicate that unsupervised meta-reinforcement learning effectively acquires accelerated reinforcement learning procedures without the need for manual task design and significantly exceeds the performance of learning from scratch. | Reject |
ICLR.cc/2022/Conference | Neural Contextual Bandits with Deep Representation and Shallow Exploration | We study neural contextual bandits, a general class of contextual bandits, where each context-action pair is associated with a raw feature vector, but the specific reward generating function is unknown. We propose a novel learning algorithm that transforms the raw feature vector using the last hidden layer of a deep ReLU neural network (deep representation learning), and uses an upper confidence bound (UCB) approach to explore in the last linear layer (shallow exploration). We prove that under standard assumptions, our proposed algorithm achieves $\tilde{O}(\sqrt{T})$ finite-time regret, where $T$ is the learning time horizon. Compared with existing neural contextual bandit algorithms, our approach is computationally much more efficient since it only needs to explore in the last layer of the deep neural network. | Accept (Poster) |
ICLR.cc/2022/Conference | Sparse MoEs meet Efficient Ensembles | Machine learning models based on the aggregated outputs of submodels, either at the activation or prediction levels, lead to strong performance. We study the interplay of two popular classes of such models: ensembles of neural networks and sparse mixture of experts (sparse MoEs). First, we show that these two approaches have complementary features whose combination is beneficial. Then, we present partitioned batch ensembles, an efficient ensemble of sparse MoEs that takes the best of both classes of models. Extensive experiments on fine-tuned vision transformers demonstrate the accuracy, log-likelihood, few-shot learning, robustness, and uncertainty calibration improvements of our approach over several challenging baselines. Partitioned batch ensembles not only scale to models with up to 2.7B parameters, but also provide larger performance gains for larger models. | Reject |
ICLR.cc/2023/Conference | An Empirical Study on the Efficacy of Deep Active Learning Techniques | Deep Active Learning (DAL) has been advocated as a promising method to reduce labeling costs in supervised learning. However, existing evaluations of DAL methods are based on different settings, and their results are controversial. To tackle this issue, this paper comprehensively evaluates 19 existing DAL methods in a uniform setting, including traditional fully-\underline{s}upervised \underline{a}ctive \underline{l}earning (SAL) strategies and emerging \underline{s}emi-\underline{s}upervised \underline{a}ctive \underline{l}earning (SSAL) techniques. We have several non-trivial findings. First, most SAL methods cannot achieve higher accuracy than random selection. Second, semi-supervised training brings significant performance improvement compared to pure SAL methods. Third, performing data selection in the SSAL setting can achieve a significant and consistent performance improvement, especially with abundant unlabeled data. Our findings produce the following guidance for practitioners: one should (i) apply SSAL as early as possible and (ii) collect more unlabeled data whenever possible, for better model performance. We will release our code upon acceptance. | Reject |
ICLR.cc/2018/Conference | Building Generalizable Agents with a Realistic and Rich 3D Environment | Teaching an agent to navigate in an unseen 3D environment is a challenging task, even in the event of simulated environments. To generalize to unseen environments, an agent needs to be robust to low-level variations (e.g. color, texture, object changes), and also high-level variations (e.g. layout changes of the environment). To improve overall generalization, all types of variations in the environment have to be taken under consideration via different level of data augmentation steps. To this end, we propose House3D, a rich, extensible and efficient environment that contains 45,622 human-designed 3D scenes of visually realistic houses, ranging from single-room studios to multi-storied houses, equipped with a diverse set of fully labeled 3D objects, textures and scene layouts, based on the SUNCG dataset (Song et al., 2017). The diversity in House3D opens the door towards scene-level augmentation, while the label-rich nature of House3D enables us to inject pixel- & task-level augmentations such as domain randomization (Tobin et al., 2017) and multi-task training. Using a subset of houses in House3D, we show that reinforcement learning agents trained with an enhancement of different levels of augmentations perform much better in unseen environments than our baselines with raw RGB input by over 8% in terms of navigation success rate. House3D is publicly available at http://github.com/facebookresearch/House3D. | Invite to Workshop Track |
ICLR.cc/2022/Conference | On Distributed Adaptive Optimization with Gradient Compression | We study COMP-AMS, a distributed optimization framework based on gradient averaging and adaptive AMSGrad algorithm. Gradient compression with error feedback is applied to reduce the communication cost in the gradient transmission process. Our convergence analysis of COMP-AMS shows that such compressed gradient averaging strategy yields same convergence rate as standard AMSGrad, and also exhibits the linear speedup effect w.r.t. the number of local workers. Compared with recently proposed protocols on distributed adaptive methods, COMP-AMS is simple and convenient. Numerical experiments are conducted to justify the theoretical findings, and demonstrate that the proposed method can achieve same test accuracy as the full-gradient AMSGrad with substantial communication savings. With its simplicity and efficiency, COMP-AMS can serve as a useful distributed training framework for adaptive methods. | Accept (Poster) |
ICLR.cc/2022/Conference | Transformers are Meta-Reinforcement Learners | The transformer architecture and variants presented a remarkable success across many machine learning tasks in recent years. This success is intrinsically related to the capability of handling long sequences and the presence of context-dependent weights from the attention mechanism. We argue that these capabilities suit the central role of a Meta-Reinforcement Learning algorithm. Indeed, a meta-RL agent needs to infer the task from a sequence of trajectories. Furthermore, it requires a fast adaptation strategy to adapt its policy for a new task - which can be achieved using the self-attention mechanism. In this work, we present TrMRL (Transformers for Meta-Reinforcement Learning), a meta-RL agent that mimics the memory reinstatement mechanism using the transformer architecture. It associates the recent past of working memories to build an episodic memory recursively through the transformer layers. This memory works as a proxy to the current task, and we condition a policy head on it. We conducted experiments in high-dimensional continuous control environments for locomotion and dexterous manipulation. Results show that TrMRL achieves or surpasses state-of-the-art performance, sample efficiency, and out-of-distribution generalization in these environments. | Reject |
ICLR.cc/2022/Conference | Heterologous Normalization | Batch Normalization has become a standard technique for training modern deep networks. However, its effectiveness diminishes when the batch size becomes smaller since the batch statistics estimation becomes inaccurate. This paper proposes Heterologous Normalization, which computes normalization's mean and standard deviation from different pixel sets to take advantage of different normalization methods. Specifically, it calculates the mean like Batch Normalization to maintain the advantage of Batch Normalization. Meanwhile, it enlarges the number of pixels from which the standard deviation is calculated, thus alleviating the problem caused by the small batch size. Experiments show that Heterologous Normalization surpasses or achieves comparable performance to existing homologous methods, with large or small batch sizes on various datasets. | Reject |
ICLR.cc/2021/Conference | Distributional Generalization: A New Kind of Generalization | We introduce a new notion of generalization--- Distributional Generalization--- which roughly states that outputs of a classifier at train and test time are close as distributions, as opposed to close in just their average error. For example, if we mislabel 30% of dogs as cats in the train set of CIFAR-10, then a ResNet trained to interpolation will in fact mislabel roughly 30% of dogs as cats on the test set as well, while leaving other classes unaffected. This behavior is not captured by classical generalization, which would only consider the average error and not the distribution of errors over the input domain. Our formal conjectures, which are much more general than this example, characterize the form of distributional generalization that can be expected in terms of problem parameters: model architecture, training procedure, number of samples, and data distribution. We give empirical evidence for these conjectures across a variety of domains in machine learning, including neural networks, kernel machines, and decision trees. Our results thus advance our understanding of interpolating classifiers. | Reject |
ICLR.cc/2019/Conference | Unsupervised Emergence of Spatial Structure from Sensorimotor Prediction | Despite its omnipresence in robotics application, the nature of spatial knowledge and the mechanisms that underlie its emergence in autonomous agents are still poorly understood. Recent theoretical work suggests that the concept of space can be grounded by capturing invariants induced by the structure of space in an agent's raw sensorimotor experience. Moreover, it is hypothesized that capturing these invariants is beneficial for a naive agent trying to predict its sensorimotor experience. Under certain exploratory conditions, spatial representations should thus emerge as a byproduct of learning to predict.
We propose a simple sensorimotor predictive scheme, apply it to different agents and types of exploration, and evaluate the pertinence of this hypothesis. We show that a naive agent can capture the topology and metric regularity of its spatial configuration without any a priori knowledge, nor extraneous supervision. | Reject |
ICLR.cc/2022/Conference | Can Label-Noise Transition Matrix Help to Improve Sample Selection and Label Correction? | Existing methods for learning with noisy labels can be generally divided into two categories: (1) sample selection and label correction based on the memorization effect of neural networks; (2) loss correction with the transition matrix. So far, the two categories of methods have been studied independently because they are designed according to different philosophies, i.e., the memorization effect is a property of the neural networks independent of label noise while the transition matrix is exploited to model the distribution of label noise. In this paper, we take a first step in unifying these two paradigms by showing that modelling the distribution of label noise with the transition matrix can also help sample selection and label correction, which leads to better robustness against different types of noise. More specifically, we first train a network with the loss corrected by the transition matrix and then use the confidence of the estimated clean class posterior from the network to select and re-label instances. Our proposed method demonstrates strong robustness on multiple benchmark datasets under various types of noise. | Withdrawn |
ICLR.cc/2019/Conference | Optimal Completion Distillation for Sequence Learning | We present Optimal Completion Distillation (OCD), a training procedure for optimizing sequence to sequence models based on edit distance. OCD is efficient, has no hyper-parameters of its own, and does not require pre-training or joint optimization with conditional log-likelihood. Given a partial sequence generated by the model, we first identify the set of optimal suffixes that minimize the total edit distance, using an efficient dynamic programming algorithm. Then, for each position of the generated sequence, we use a target distribution which puts equal probability on the first token of all the optimal suffixes. OCD achieves the state-of-the-art performance on end-to-end speech recognition, on both Wall Street Journal and Librispeech datasets, achieving $9.3\%$ WER and $4.5\%$ WER, respectively. | Accept (Poster) |
ICLR.cc/2020/Conference | Effective Use of Variational Embedding Capacity in Expressive End-to-End Speech Synthesis | Recent work has explored sequence-to-sequence latent variable models for expressive speech synthesis (supporting control and transfer of prosody and style), but has not presented a coherent framework for understanding the trade-offs between the competing methods. In this paper, we propose embedding capacity (the amount of information the embedding contains about the data) as a unified method of analyzing the behavior of latent variable models of speech, comparing existing heuristic (non-variational) methods to variational methods that are able to explicitly constrain capacity using an upper bound on representational mutual information. In our proposed model (Capacitron), we show that by adding conditional dependencies to the variational posterior such that it matches the form of the true posterior, the same model can be used for high-precision prosody transfer, text-agnostic style transfer, and generation of natural-sounding prior samples. For multi-speaker models, Capacitron is able to preserve target speaker identity during inter-speaker prosody transfer and when drawing samples from the latent prior. Lastly, we introduce a method for decomposing embedding capacity hierarchically across two sets of latents, allowing a portion of the latent variability to be specified and the remaining variability sampled from a learned prior. Audio examples are available on the web. | Reject |
ICLR.cc/2023/Conference | Compressing multidimensional weather and climate data into neural networks | Weather and climate simulations produce petabytes of high-resolution data that are later analyzed by researchers in order to understand climate change or severe weather. We propose a new method of compressing this multidimensional weather and climate data: a coordinate-based neural network is trained to overfit the data, and the resulting parameters are taken as a compact representation of the original grid-based data. While compression ratios range from 300x to more than 3,000x, our method outperforms the state-of-the-art compressor SZ3 in terms of weighted RMSE, MAE. It can faithfully preserve important large scale atmosphere structures and does not introduce significant artifacts.
When using the resulting neural network as a 790x compressed dataloader to train the WeatherBench forecasting model, its RMSE increases by less than 2%. The three orders of magnitude compression democratizes access to high-resolution climate data and enables numerous new research directions. | Accept: notable-top-5% |
ICLR.cc/2022/Conference | KNIFE: Kernelized-Neural Differential Entropy Estimation | Estimation of (differential) entropy and the related mutual information has been pursued with significant efforts by the machine learning community. To address shortcomings in previously proposed estimators for differential entropy, here we introduce KNIFE, a fully parameterized, differentiable kernel-based estimator of differential entropy. The flexibility of our approach also allows us to construct KNIFE-based estimators for conditional (on either discrete or continuous variables) differential entropy, as well as mutual information. We empirically validate our method on high-dimensional synthetic data and further apply it to guide the training of neural networks for real-world tasks. Our experiments on a large variety of tasks, including visual domain adaptation, textual fair classification, and textual fine-tuning demonstrate the effectiveness of KNIFE-based estimation. | Reject |
ICLR.cc/2022/Conference | A Unified Wasserstein Distributional Robustness Framework for Adversarial Training | It is well-known that deep neural networks (DNNs) are susceptible to adversarial attacks, exposing a severe fragility of deep learning systems. As the result, adversarial training (AT) method, by incorporating adversarial examples during training, represents a natural and effective approach to strengthen the robustness of a DNN-based classifier. However, most AT-based methods, notably PGD-AT and TRADES, typically seek a pointwise adversary that generates the worst-case adversarial example by independently perturbing each data sample, as a way to ``probe'' the vulnerability of the classifier. Arguably, there are unexplored benefits in considering such adversarial effects from an entire distribution. To this end, this paper presents a unified framework that connects Wasserstein distributional robustness with current state-of-the-art AT methods. We introduce a new Wasserstein cost function and a new series of risk functions, with which we show that standard AT methods are special cases of their counterparts in our framework. This connection leads to an intuitive relaxation and generalization of existing AT methods and facilitates the development of a new family of distributional robustness AT-based algorithms. Extensive experiments show that our distributional robustness AT algorithms robustify further their standard AT counterparts in various settings. | Accept (Poster) |
ICLR.cc/2022/Conference | Towards Robust Point Cloud Models with Context-Consistency Network and Adaptive Augmentation | 3D point cloud models based on deep neural networks were proven to be vulnerable to adversarial examples, with a quantity of novel attack techniques proposed by researchers recently. It is of paramount importance to preserve the robustness of 3D models under adversarial environments, considering their broad application in safety- and security-critical tasks. Unfortunately, defenses for 3D models are much less studied compared to 2D image models. In this paper, we reason about the vulnerability of 3D models based on the mutual information theory. Furthermore, we design an effective defense methodology, consisting of two innovations. (1) We introduce CCDGN, a novel 3D DNN architecture which includes robust and light-weight modules to alleviate adversarial examples. (2) We propose AA-AMS a novel data augmentation strategy to adaptively balance the model usability and robustness. Extensive evaluations indicate the integration of the two techniques provides much more robustness than existing defense solutions for 3D models. | Withdrawn |
ICLR.cc/2023/Conference | Examining the Difference Among Transformers and CNNs with Explanation Methods | We propose a methodology that systematically applies deep explanation algorithms on a dataset-wide basis, to compare different types of visual recognition backbones, such as convolutional networks (CNNs), global attention networks, and local attention networks. We examine both qualitative visualizations and quantitative statistics across the dataset, in order to generate intuitions that are not just anecdotal, but are supported by the statistics computed on the whole dataset. Specifically, we propose two methods. The first one, sub-explanation counting, systematically searches for minimally-sufficient explanations of all images and count the amount of sub-explanations for each network. The second one, called cross-testing, computes salient regions using one network and then evaluates the performance by only showing these regions as an image to other networks. Through a combination of qualitative insights and quantitative statistics, we illustrate that 1) there are significant differences between the salient features of CNNs and attention models; 2) the occlusion-robustness in local attention models and global attention models may come from different decision-making mechanisms. | Withdrawn |
ICLR.cc/2018/Conference | Dynamic Integration of Background Knowledge in Neural NLU Systems | Common-sense or background knowledge is required to understand natural language, but in most neural natural language understanding (NLU) systems, the requisite background knowledge is indirectly acquired from static corpora. We develop a new reading architecture for the dynamic integration of explicit background knowledge in NLU models. A new task-agnostic reading module provides refined word representations to a task-specific NLU architecture by processing background knowledge in the form of free-text statements, together with the task-specific inputs. Strong performance on the tasks of document question answering (DQA) and recognizing textual entailment (RTE) demonstrate the effectiveness and flexibility of our approach. Analysis shows that our models learn to exploit knowledge selectively and in a semantically appropriate way. | Reject |
ICLR.cc/2023/Conference | NeRF-SOS: Any-View Self-supervised Object Segmentation on Complex Scenes | Neural volumetric representations have shown the potential that Multi-layer Perceptrons (MLPs) can be optimized with multi-view calibrated images to represent scene geometry and appearance without explicit 3D supervision. Object segmentation can enrich many downstream applications based on the learned radiance field. However, introducing hand-crafted segmentation to define regions of interest in a complex real-world scene is non-trivial and expensive as it acquires per view annotation. This paper carries out the exploration of self-supervised learning for object segmentation using NeRF for complex real-world scenes. Our framework, called NeRF with Self-supervised Object Segmentation (NeRF-SOS), couples object segmentation and neural radiance field to segment objects in any view within a scene. By proposing a novel collaborative contrastive loss in both appearance and geometry levels, NeRF-SOS encourages NeRF models to distill compact geometry-aware segmentation clusters from their density fields and the self-supervised pre-trained 2D visual features. The self-supervised object segmentation framework can be applied to various NeRF models that both lead to photo-realistic rendering results and convincing segmentation maps for both indoor and outdoor scenarios. Extensive results on the LLFF, BlendedMVS, CO3Dv2, and Tank & Temples datasets validate the effectiveness of NeRF-SOS. It consistently surpasses other 2D-based self-supervised baselines and predicts finer object masks than existing supervised counterparts. | Accept: poster |
ICLR.cc/2022/Conference | A General Unified Graph Neural Network Framework Against Adversarial Attacks | Graph Neural Networks (GNNs) are powerful tools in representation learning for graphs. However, they are reported to be vulnerable to adversarial attacks, raising numerous concerns for applying it in some risk-sensitive domains. Therefore, it is essential to develop a robust GNN model to defend against adversarial attacks. Existing studies address this issue only considering cleaning perturbed graph structure, and almost none of them simultaneously consider denoising features. As the graph and features are interrelated and influence each other, we propose a General Unified Graph Neural Network (GUGNN) framework to jointly clean the graph and denoise features of data. On this basis, we further extend it by introducing two operations and develop a robust GNN model(R-GUGNN) to defend against adversarial attacks. One operation is reconstructing the graph with its intrinsic properties, including similarity of two adjacent nodes’ features, sparsity of real-world graphs and many slight noises having small eigenvalues in perturbed graphs. The other is the convolution operation for features to find the optimal solution adopting the Laplacian smoothness and the prior knowledge that nodes with many neighbors are difficult to attack. Experiments on four real-world datasets demonstrate that R-GUGNN has greatly improved the overall robustness over the state-of-the-art baselines. | Reject |