title
stringlengths 19
143
| url
stringlengths 41
43
| detail_url
stringlengths 41
43
| authors
stringlengths 9
347
| tags
stringclasses 3
values | abstract
stringlengths 457
2.38k
| pdf
stringlengths 71
71
|
|---|---|---|---|---|---|---|
ChipNet: Budget-Aware Pruning with Heaviside Continuous Approximations | https://openreview.net/forum?id=xCxXwTzx4L1 | https://openreview.net/forum?id=xCxXwTzx4L1 | Rishabh Tiwari,Udbhav Bamba,Arnav Chavan,Deepak Gupta | ICLR 2021,Poster | Structured pruning methods are among the effective strategies for extracting small resource-efficient convolutional neural networks from their dense counterparts with minimal loss in accuracy. However, most existing methods still suffer from one or more limitations, that include 1) the need for training the dense model from scratch with pruning-related parameters embedded in the architecture, 2) requiring model-specific hyperparameter settings, 3) inability to include budget-related constraint in the training process, and 4) instability under scenarios of extreme pruning. In this paper, we present ChipNet, a deterministic pruning strategy that employs continuous Heaviside function and a novel crispness loss to identify a highly sparse network out of an existing dense network. Our choice of continuous Heaviside function is inspired by the field of design optimization, where the material distribution task is posed as a continuous optimization problem, but only discrete values (0 or 1) are practically feasible and expected as final outcomes. Our approach's flexible design facilitates its use with different choices of budget constraints while maintaining stability for very low target budgets. Experimental results show that ChipNet outperforms state-of-the-art structured pruning methods by remarkable margins of up to 16.1% in terms of accuracy. Further, we show that the masks obtained with ChipNet are transferable across datasets. For certain cases, it was observed that masks transferred from a model trained on feature-rich teacher dataset provide better performance on the student dataset than those obtained by directly pruning on the student data itself. | https://openreview.net/pdf/f3ff08ec88e7c8f10d78c77e44d65384b446c064.pdf |
AdaSpeech: Adaptive Text to Speech for Custom Voice | https://openreview.net/forum?id=Drynvt7gg4L | https://openreview.net/forum?id=Drynvt7gg4L | Mingjian Chen,Xu Tan,Bohan Li,Yanqing Liu,Tao Qin,sheng zhao,Tie-Yan Liu | ICLR 2021,Poster | Custom voice, a specific text to speech (TTS) service in commercial speech platforms, aims to adapt a source TTS model to synthesize personal voice for a target speaker using few speech from her/him. Custom voice presents two unique challenges for TTS adaptation: 1) to support diverse customers, the adaptation model needs to handle diverse acoustic conditions which could be very different from source speech data, and 2) to support a large number of customers, the adaptation parameters need to be small enough for each target speaker to reduce memory usage while maintaining high voice quality. In this work, we propose AdaSpeech, an adaptive TTS system for high-quality and efficient customization of new voices. We design several techniques in AdaSpeech to address the two challenges in custom voice: 1) To handle different acoustic conditions, we model the acoustic information in both utterance and phoneme level. Specifically, we use one acoustic encoder to extract an utterance-level vector and another one to extract a sequence of phoneme-level vectors from the target speech during pre-training and fine-tuning; in inference, we extract the utterance-level vector from a reference speech and use an acoustic predictor to predict the phoneme-level vectors. 2) To better trade off the adaptation parameters and voice quality, we introduce conditional layer normalization in the mel-spectrogram decoder of AdaSpeech, and fine-tune this part in addition to speaker embedding for adaptation. We pre-train the source TTS model on LibriTTS datasets and fine-tune it on VCTK and LJSpeech datasets (with different acoustic conditions from LibriTTS) with few adaptation data, e.g., 20 sentences, about 1 minute speech. Experiment results show that AdaSpeech achieves much better adaptation quality than baseline methods, with only about 5K specific parameters for each speaker, which demonstrates its effectiveness for custom voice. The audio samples are available at https://speechresearch.github.io/adaspeech/. | https://openreview.net/pdf/a106470a7fdf6a1a5575c27c3e1b22bbc5ec2fa6.pdf |
Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors | https://openreview.net/forum?id=YLewtnvKgR7 | https://openreview.net/forum?id=YLewtnvKgR7 | Ali Harakeh,Steven L. Waslander | ICLR 2021,Poster | Predictive uncertainty estimation is an essential next step for the reliable deployment of deep object detectors in safety-critical tasks. In this work, we focus on estimating predictive distributions for bounding box regression output with variance networks. We show that in the context of object detection, training variance networks with negative log likelihood (NLL) can lead to high entropy predictive distributions regardless of the correctness of the output mean. We propose to use the energy score as a non-local proper scoring rule and find that when used for training, the energy score leads to better calibrated and lower entropy predictive distributions than NLL. We also address the widespread use of non-proper scoring metrics for evaluating predictive distributions from deep object detectors by proposing an alternate evaluation approach founded on proper scoring rules. Using the proposed evaluation tools, we show that although variance networks can be used to produce high quality predictive distributions, ad-hoc approaches used by seminal object detectors for choosing regression targets during training do not provide wide enough data support for reliable variance learning. We hope that our work helps shift evaluation in probabilistic object detection to better align with predictive uncertainty evaluation in other machine learning domains. Code for all models, evaluation, and datasets is available at: https://github.com/asharakeh/probdet.git. | https://openreview.net/pdf/6a677ce1f598db1f6e966821150e994540b89717.pdf |
Domain-Robust Visual Imitation Learning with Mutual Information Constraints | https://openreview.net/forum?id=QubpWYfdNry | https://openreview.net/forum?id=QubpWYfdNry | Edoardo Cetin,Oya Celiktutan | ICLR 2021,Poster | Human beings are able to understand objectives and learn by simply observing others perform a task. Imitation learning methods aim to replicate such capabilities, however, they generally depend on access to a full set of optimal states and actions taken with the agent's actuators and from the agent's point of view. In this paper, we introduce a new algorithm - called Disentangling Generative Adversarial Imitation Learning (DisentanGAIL) - with the purpose of bypassing such constraints. Our algorithm enables autonomous agents to learn directly from high dimensional observations of an expert performing a task, by making use of adversarial learning with a latent representation inside the discriminator network. Such latent representation is regularized through mutual information constraints to incentivize learning only features that encode information about the completion levels of the task being demonstrated. This allows to obtain a shared feature space to successfully perform imitation while disregarding the differences between the expert's and the agent's domains. Empirically, our algorithm is able to efficiently imitate in a diverse range of control problems including balancing, manipulation and locomotive tasks, while being robust to various domain differences in terms of both environment appearance and agent embodiment. | https://openreview.net/pdf/ffeade3d551ddc81dea27db706160b3bc6510cec.pdf |
Clustering-friendly Representation Learning via Instance Discrimination and Feature Decorrelation | https://openreview.net/forum?id=e12NDM7wkEY | https://openreview.net/forum?id=e12NDM7wkEY | Yaling Tao,Kentaro Takagi,Kouta Nakata | ICLR 2021,Poster | Clustering is one of the most fundamental tasks in machine learning. Recently, deep clustering has become a major trend in clustering techniques. Representation learning often plays an important role in the effectiveness of deep clustering, and thus can be a principal cause of performance degradation. In this paper, we propose a clustering-friendly representation learning method using instance discrimination and feature decorrelation. Our deep-learning-based representation learning method is motivated by the properties of classical spectral clustering. Instance discrimination learns similarities among data and feature decorrelation removes redundant correlation among features. We utilize an instance discrimination method in which learning individual instance classes leads to learning similarity among instances. Through detailed experiments and examination, we show that the approach can be adapted to learning a latent space for clustering. We design novel softmax-formulated decorrelation constraints for learning. In evaluations of image clustering using CIFAR-10 and ImageNet-10, our method achieves accuracy of 81.5% and 95.4%, respectively. We also show that the softmax-formulated constraints are compatible with various neural networks. | https://openreview.net/pdf/a66aec8c00bfa723a6c8e4690bd00cf755e17e8f.pdf |
Progressive Skeletonization: Trimming more fat from a network at initialization | https://openreview.net/forum?id=9GsFOUyUPi | https://openreview.net/forum?id=9GsFOUyUPi | Pau de Jorge,Amartya Sanyal,Harkirat Behl,Philip Torr,Grégory Rogez,Puneet K. Dokania | ICLR 2021,Poster | Recent studies have shown that skeletonization (pruning parameters) of networks at initialization provides all the practical benefits of sparsity both at inference and training time, while only marginally degrading their performance. However, we observe that beyond a certain level of sparsity (approx 95%), these approaches fail to preserve the network performance, and to our surprise, in many cases perform even worse than trivial random pruning. To this end, we propose an objective to find a skeletonized network with maximum foresight connection sensitivity (FORCE) whereby the trainability, in terms of connection sensitivity, of a pruned network is taken into consideration. We then propose two approximate procedures to maximize our objective (1) Iterative SNIP: allows parameters that were unimportant at earlier stages of skeletonization to become important at later stages; and (2) FORCE: iterative process that allows exploration by allowing already pruned parameters to resurrect at later stages of skeletonization. Empirical analysis on a large suite of experiments show that our approach, while providing at least as good performance as other recent approaches on moderate pruning levels, provide remarkably improved performance on high pruning levels (could remove up to 99.5% parameters while keeping the networks trainable). | https://openreview.net/pdf/c774cc8cddefe7ca854f3b051602f706c05b7239.pdf |
On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis | https://openreview.net/forum?id=8Sqhl-nF50 | https://openreview.net/forum?id=8Sqhl-nF50 | Zhong Li,Jiequn Han,Weinan E,Qianxiao Li | ICLR 2021,Poster | We study the approximation properties and optimization dynamics of recurrent neural networks (RNNs) when applied to learn input-output relationships in temporal data. We consider the simple but representative setting of using continuous-time linear RNNs to learn from data generated by linear relationships. Mathematically, the latter can be understood as a sequence of linear functionals. We prove a universal approximation theorem of such linear functionals and characterize the approximation rate. Moreover, we perform a fine-grained dynamical analysis of training linear RNNs by gradient methods. A unifying theme uncovered is the non-trivial effect of memory, a notion that can be made precise in our framework, on both approximation and optimization: when there is long-term memory in the target, it takes a large number of neurons to approximate it. Moreover, the training process will suffer from slow downs. In particular, both of these effects become exponentially more pronounced with increasing memory - a phenomenon we call the “curse of memory”. These analyses represent a basic step towards a concrete mathematical understanding of new phenomenons that may arise in learning temporal relationships using recurrent architectures. | https://openreview.net/pdf/1572c344c730b678160659f1c3497d51342252bd.pdf |
Do not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning | https://openreview.net/forum?id=7aogOj_VYO0 | https://openreview.net/forum?id=7aogOj_VYO0 | Da Yu,Huishuai Zhang,Wei Chen,Tie-Yan Liu | ICLR 2021,Poster | The privacy leakage of the model about the training data can be bounded in the differential privacy mechanism. However, for meaningful privacy parameters, a differentially private model degrades the utility drastically when the model comprises a large number of trainable parameters. In this paper, we propose an algorithm \emph{Gradient Embedding Perturbation (GEP)} towards training differentially private deep models with decent accuracy. Specifically, in each gradient descent step, GEP first projects individual private gradient into a non-sensitive anchor subspace, producing a low-dimensional gradient embedding and a small-norm residual gradient. Then, GEP perturbs the low-dimensional embedding and the residual gradient separately according to the privacy budget. Such a decomposition permits a small perturbation variance, which greatly helps to break the dimensional barrier of private learning. With GEP, we achieve decent accuracy with low computational cost and modest privacy guarantee for deep models. Especially, with privacy bound $\epsilon=8$, we achieve $74.9\%$ test accuracy on CIFAR10 and $95.1\%$ test accuracy on SVHN, significantly improving over existing results. | https://openreview.net/pdf/d70f5682ba3c5d03cd82bb7d287b19e150c1f9a5.pdf |
More or Less: When and How to Build Convolutional Neural Network Ensembles | https://openreview.net/forum?id=z5Z023VBmDZ | https://openreview.net/forum?id=z5Z023VBmDZ | Abdul Wasay,Stratos Idreos | ICLR 2021,Poster | Convolutional neural networks are utilized to solve increasingly more complex problems and with more data. As a result, researchers and practitioners seek to scale the representational power of such models by adding more parameters. However, increasing parameters requires additional critical resources in terms of memory and compute, leading to increased training and inference cost. Thus a consistent challenge is to obtain as high as possible accuracy within a parameter budget. As neural network designers navigate this complex landscape, they are guided by conventional wisdom that is informed from past empirical studies. We identify a critical part of this design space that is not well-understood: How to decide between the alternatives of expanding a single convolutional network model or increasing the number of networks in the form of an ensemble. We study this question in detail across various network architectures and data sets. We build an extensive experimental framework that captures numerous angles of the possible design space in terms of how a new set of parameters can be used in a model. We consider a holistic set of metrics such as training time, inference time, and memory usage. The framework provides a robust assessment by making sure it controls for the number of parameters. Contrary to conventional wisdom, we show that when we perform a holistic and robust assessment, we uncover a wide design space, where ensembles provide better accuracy, train faster, and deploy at speed comparable to single convolutional networks with the same total number of parameters. | https://openreview.net/pdf/e955ea07ee1a66ac3cdcfc11c93b7cb87732b0d8.pdf |
Revisiting Hierarchical Approach for Persistent Long-Term Video Prediction | https://openreview.net/forum?id=3RLN4EPMdYd | https://openreview.net/forum?id=3RLN4EPMdYd | Wonkwang Lee,Whie Jung,Han Zhang,Ting Chen,Jing Yu Koh,Thomas Huang,Hyungsuk Yoon,Honglak Lee,Seunghoon Hong | ICLR 2021,Poster | Learning to predict the long-term future of video frames is notoriously challenging due to the inherent ambiguities in a distant future and dramatic amplification of prediction error over time. Despite the recent advances in the literature, existing approaches are limited to moderately short-term prediction (less than a few seconds), while extrapolating it to a longer future quickly leads to destruction in structure and content. In this work, we revisit the hierarchical models in video prediction. Our method generates future frames by first estimating a sequence of dense semantic structures and subsequently translating the estimated structures to pixels by video-to-video translation model. Despite the simplicity, we show that modeling structures and their dynamics in categorical structure space with stochastic sequential estimator leads to surprisingly successful long-term prediction. We evaluate our method on two challenging video prediction scenarios, \emph{car driving} and \emph{human dancing}, and demonstrate that it can generate complicated scene structures and motions over a very long time horizon (\ie~thousands frames), setting a new standard of video prediction with orders of magnitude longer prediction time than existing approaches. Video results are available at https://1konny.github.io/HVP/. | https://openreview.net/pdf/e863c32a3e7f4b5e24c3ad1dc8f345da8ba3bdd2.pdf |
Symmetry-Aware Actor-Critic for 3D Molecular Design | https://openreview.net/forum?id=jEYKjPE1xYN | https://openreview.net/forum?id=jEYKjPE1xYN | Gregor N. C. Simm,Robert Pinsler,Gábor Csányi,José Miguel Hernández-Lobato | ICLR 2021,Poster | Automating molecular design using deep reinforcement learning (RL) has the potential to greatly accelerate the search for novel materials. Despite recent progress on leveraging graph representations to design molecules, such methods are fundamentally limited by the lack of three-dimensional (3D) information. In light of this, we propose a novel actor-critic architecture for 3D molecular design that can generate molecular structures unattainable with previous approaches. This is achieved by exploiting the symmetries of the design process through a rotationally covariant state-action representation based on a spherical harmonics series expansion. We demonstrate the benefits of our approach on several 3D molecular design tasks, where we find that building in such symmetries significantly improves generalization and the quality of generated molecules. | https://openreview.net/pdf/910f34d86d427e8bfed4af189076b9d497578643.pdf |
Learnable Embedding sizes for Recommender Systems | https://openreview.net/forum?id=vQzcqQWIS0q | https://openreview.net/forum?id=vQzcqQWIS0q | Siyi Liu,Chen Gao,Yihong Chen,Depeng Jin,Yong Li | ICLR 2021,Poster | The embedding-based representation learning is commonly used in deep learning recommendation models to map the raw sparse features to dense vectors. The traditional embedding manner that assigns a uniform size to all features has two issues. First, the numerous features inevitably lead to a gigantic embedding table that causes a high memory usage cost. Second, it is likely to cause the over-fitting problem for those features that do not require too large representation capacity. Existing works that try to address the problem always cause a significant drop in recommendation performance or suffers from the limitation of unaffordable training time cost. In this paper, we proposed a novel approach, named PEP (short for Plug-in Embedding Pruning), to reduce the size of the embedding table while avoiding the drop of recommendation accuracy. PEP prunes embedding parameter where the pruning threshold(s) can be adaptively learned from data. Therefore we can automatically obtain a mixed-dimension embedding-scheme by pruning redundant parameters for each feature. PEP is a general framework that can plug in various base recommendation models. Extensive experiments demonstrate it can efficiently cut down embedding parameters and boost the base model's performance. Specifically, it achieves strong recommendation performance while reducing 97-99% parameters. As for the computation cost, PEP only brings an additional 20-30% time cost compare with base models. | https://openreview.net/pdf/57ca06dcb49d0ba8a6c705fcd2a34d58a7cf8c3a.pdf |
Learning Neural Generative Dynamics for Molecular Conformation Generation | https://openreview.net/forum?id=pAbm1qfheGk | https://openreview.net/forum?id=pAbm1qfheGk | Minkai Xu,Shitong Luo,Yoshua Bengio,Jian Peng,Jian Tang | ICLR 2021,Poster | We study how to generate molecule conformations (i.e., 3D structures) from a molecular graph. Traditional methods, such as molecular dynamics, sample conformations via computationally expensive simulations. Recently, machine learning methods have shown great potential by training on a large collection of conformation data. Challenges arise from the limited model capacity for capturing complex distributions of conformations and the difficulty in modeling long-range dependencies between atoms. Inspired by the recent progress in deep generative models, in this paper, we propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph. We propose a method combining the advantages of both flow-based and energy-based models, enjoying: (1) a high model capacity to estimate the multimodal conformation distribution; (2) explicitly capturing the complex long-range dependencies between atoms in the observation space. Extensive experiments demonstrate the superior performance of the proposed method on several benchmarks, including conformation generation and distance modeling tasks, with a significant improvement over existing generative models for molecular conformation sampling. | https://openreview.net/pdf/90d50e0ca739c22ebb906023d446dc8c8f98a7e0.pdf |
Learning the Pareto Front with Hypernetworks | https://openreview.net/forum?id=NjF772F4ZZR | https://openreview.net/forum?id=NjF772F4ZZR | Aviv Navon,Aviv Shamsian,Ethan Fetaya,Gal Chechik | ICLR 2021,Poster | Multi-objective optimization (MOO) problems are prevalent in machine learning. These problems have a set of optimal solutions, called the Pareto front, where each point on the front represents a different trade-off between possibly conflicting objectives. Recent MOO methods can target a specific desired ray in loss space however, most approaches still face two grave limitations: (i) A separate model has to be trained for each point on the front; and (ii) The exact trade-off must be known before the optimization process. Here, we tackle the problem of learning the entire Pareto front, with the capability of selecting a desired operating point on the front after training. We call this new setup Pareto-Front Learning (PFL).
We describe an approach to PFL implemented using HyperNetworks, which we term Pareto HyperNetworks (PHNs). PHN learns the entire Pareto front simultaneously using a single hypernetwork, which receives as input a desired preference vector and returns a Pareto-optimal model whose loss vector is in the desired ray. The unified model is runtime efficient compared to training multiple models and generalizes to new operating points not used during training. We evaluate our method on a wide set of problems, from multi-task regression and classification to fairness. PHNs learn the entire Pareto front at roughly the same time as learning a single point on the front and at the same time reach a better solution set. PFL opens the door to new applications where models are selected based on preferences that are only available at run time. | https://openreview.net/pdf/9c01e8c47f7e80e87af0175ac2a5e9a356f518bd.pdf |
Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning | https://openreview.net/forum?id=Y87Ri-GNHYu | https://openreview.net/forum?id=Y87Ri-GNHYu | Valerie Chen,Abhinav Gupta,Kenneth Marino | ICLR 2021,Poster | Complex, multi-task problems have proven to be difficult to solve efficiently in a sparse-reward reinforcement learning setting. In order to be sample efficient, multi-task learning requires reuse and sharing of low-level policies. To facilitate the automatic decomposition of hierarchical tasks, we propose the use of step-by-step human demonstrations in the form of natural language instructions and action trajectories. We introduce a dataset of such demonstrations in a crafting-based grid world. Our model consists of a high-level language generator and low-level policy, conditioned on language. We find that human demonstrations help solve the most complex tasks. We also find that incorporating natural language allows the model to generalize to unseen tasks in a zero-shot setting and to learn quickly from a few demonstrations. Generalization is not only reflected in the actions of the agent, but also in the generated natural language instructions in unseen tasks. Our approach also gives our trained agent interpretable behaviors because it is able to generate a sequence of high-level descriptions of its actions. | https://openreview.net/pdf/be28b5c6ceded7efd434a4a312e5019c4cb5480f.pdf |
Taming GANs with Lookahead-Minmax | https://openreview.net/forum?id=ZW0yXJyNmoG | https://openreview.net/forum?id=ZW0yXJyNmoG | Tatjana Chavdarova,Matteo Pagliardini,Sebastian U Stich,François Fleuret,Martin Jaggi | ICLR 2021,Poster | Generative Adversarial Networks are notoriously challenging to train. The underlying minmax optimization is highly susceptible to the variance of the stochastic gradient and the rotational component of the associated game vector field. To tackle these challenges, we propose the Lookahead algorithm for minmax optimization, originally developed for single objective minimization only. The backtracking step of our Lookahead–minmax naturally handles the rotational game dynamics, a property which was identified to be key for enabling gradient ascent descent methods to converge on challenging examples often analyzed in the literature. Moreover, it implicitly handles high variance without using large mini-batches, known to be essential for reaching state of the art performance. Experimental results on MNIST, SVHN, CIFAR-10, and ImageNet demonstrate a clear advantage of combining Lookahead–minmax with Adam or extragradient, in terms of performance and improved stability, for negligible memory and computational cost. Using 30-fold fewer parameters and 16-fold smaller minibatches we outperform the reported performance of the class-dependent BigGAN on CIFAR-10 by obtaining FID of 12.19 without using the class labels, bringing state-of-the-art GAN training within reach of common computational resources. | https://openreview.net/pdf/1e1819f3ec50eb22f92cb7e0e6a5ff0d1c7d458a.pdf |
Uncertainty in Gradient Boosting via Ensembles | https://openreview.net/forum?id=1Jv6b0Zq3qi | https://openreview.net/forum?id=1Jv6b0Zq3qi | Andrey Malinin,Liudmila Prokhorenkova,Aleksei Ustimenko | ICLR 2021,Poster | For many practical, high-risk applications, it is essential to quantify uncertainty in a model's predictions to avoid costly mistakes. While predictive uncertainty is widely studied for neural networks, the topic seems to be under-explored for models based on gradient boosting. However, gradient boosting often achieves state-of-the-art results on tabular data. This work examines a probabilistic ensemble-based framework for deriving uncertainty estimates in the predictions of gradient boosting classification and regression models. We conducted experiments on a range of synthetic and real datasets and investigated the applicability of ensemble approaches to gradient boosting models that are themselves ensembles of decision trees. Our analysis shows that ensembles of gradient boosting models successfully detect anomalous inputs while having limited ability to improve the predicted total uncertainty. Importantly, we also propose a concept of a virtual ensemble to get the benefits of an ensemble via only one gradient boosting model, which significantly reduces complexity. | https://openreview.net/pdf/d20bb72ed1f0c69fa43f4363ec90050b0e79af3d.pdf |
Adversarial score matching and improved sampling for image generation | https://openreview.net/forum?id=eLfqMl3z3lq | https://openreview.net/forum?id=eLfqMl3z3lq | Alexia Jolicoeur-Martineau,Rémi Piché-Taillefer,Ioannis Mitliagkas,Remi Tachet des Combes | ICLR 2021,Poster | Denoising Score Matching with Annealed Langevin Sampling (DSM-ALS) has recently found success in generative modeling. The approach works by first training a neural network to estimate the score of a distribution, and then using Langevin dynamics to sample from the data distribution assumed by the score network. Despite the convincing visual quality of samples, this method appears to perform worse than Generative Adversarial Networks (GANs) under the Fréchet Inception Distance, a standard metric for generative models. We show that this apparent gap vanishes when denoising the final Langevin samples using the score network.
In addition, we propose two improvements to DSM-ALS: 1) Consistent Annealed Sampling as a more stable alternative to Annealed Langevin Sampling, and 2) a hybrid training formulation, composed of both Denoising Score Matching and adversarial objectives. By combining these two techniques and exploring different network architectures, we elevate score matching methods and obtain results competitive with state-of-the-art image generation on CIFAR-10. | https://openreview.net/pdf/0736f85c2423935a24eac2b7bf463207eb3fa02a.pdf |
FastSpeech 2: Fast and High-Quality End-to-End Text to Speech | https://openreview.net/forum?id=piLPYqxtWuA | https://openreview.net/forum?id=piLPYqxtWuA | Yi Ren,Chenxu Hu,Xu Tan,Tao Qin,Sheng Zhao,Zhou Zhao,Tie-Yan Liu | ICLR 2021,Poster | Non-autoregressive text to speech (TTS) models such as FastSpeech can synthesize speech significantly faster than previous autoregressive models with comparable quality. The training of FastSpeech model relies on an autoregressive teacher model for duration prediction (to provide more information as input) and knowledge distillation (to simplify the data distribution in output), which can ease the one-to-many mapping problem (i.e., multiple speech variations correspond to the same text) in TTS. However, FastSpeech has several disadvantages: 1) the teacher-student distillation pipeline is complicated and time-consuming, 2) the duration extracted from the teacher model is not accurate enough, and the target mel-spectrograms distilled from teacher model suffer from information loss due to data simplification, both of which limit the voice quality. In this paper, we propose FastSpeech 2, which addresses the issues in FastSpeech and better solves the one-to-many mapping problem in TTS by 1) directly training the model with ground-truth target instead of the simplified output from teacher, and 2) introducing more variation information of speech (e.g., pitch, energy and more accurate duration) as conditional inputs. Specifically, we extract duration, pitch and energy from speech waveform and directly take them as conditional inputs in training and use predicted values in inference. We further design FastSpeech 2s, which is the first attempt to directly generate speech waveform from text in parallel, enjoying the benefit of fully end-to-end inference. Experimental results show that 1) FastSpeech 2 achieves a 3x training speed-up over FastSpeech, and FastSpeech 2s enjoys even faster inference speed; 2) FastSpeech 2 and 2s outperform FastSpeech in voice quality, and FastSpeech 2 can even surpass autoregressive models. Audio samples are available at https://speechresearch.github.io/fastspeech2/. | https://openreview.net/pdf/16d889999a70fcdfe5fbe9165d7d341e47b05e93.pdf |
Interpretable Models for Granger Causality Using Self-explaining Neural Networks | https://openreview.net/forum?id=DEa4JdMWRHp | https://openreview.net/forum?id=DEa4JdMWRHp | Ričards Marcinkevičs,Julia E Vogt | ICLR 2021,Poster | Exploratory analysis of time series data can yield a better understanding of complex dynamical systems. Granger causality is a practical framework for analysing interactions in sequential data, applied in a wide range of domains. In this paper, we propose a novel framework for inferring multivariate Granger causality under nonlinear dynamics based on an extension of self-explaining neural networks. This framework is more interpretable than other neural-network-based techniques for inferring Granger causality, since in addition to relational inference, it also allows detecting signs of Granger-causal effects and inspecting their variability over time. In comprehensive experiments on simulated data, we show that our framework performs on par with several powerful baseline methods at inferring Granger causality and that it achieves better performance at inferring interaction signs. The results suggest that our framework is a viable and more interpretable alternative to sparse-input neural networks for inferring Granger causality. | https://openreview.net/pdf/9adb4d4523a376d9afeece07cffb8e319f8cc687.pdf |
Learning Deep Features in Instrumental Variable Regression | https://openreview.net/forum?id=sy4Kg_ZQmS7 | https://openreview.net/forum?id=sy4Kg_ZQmS7 | Liyuan Xu,Yutian Chen,Siddarth Srinivasan,Nando de Freitas,Arnaud Doucet,Arthur Gretton | ICLR 2021,Poster | Instrumental variable (IV) regression is a standard strategy for learning causal relationships between confounded treatment and outcome variables from observational data by using an instrumental variable, which affects the outcome only through the treatment. In classical IV regression, learning proceeds in two stages: stage 1 performs linear regression from the instrument to the treatment; and stage 2 performs linear regression from the treatment to the outcome, conditioned on the instrument. We propose a novel method, deep feature instrumental variable regression (DFIV), to address the case where relations between instruments, treatments, and outcomes may be nonlinear. In this case, deep neural nets are trained to define informative nonlinear features on the instruments and treatments. We propose an alternating training regime for these features to ensure good end-to-end performance when composing stages 1 and 2, thus obtaining highly flexible feature maps in a computationally efficient manner.
DFIV outperforms recent state-of-the-art methods on challenging IV benchmarks, including settings involving high dimensional image data. DFIV also exhibits competitive performance in off-policy policy evaluation for reinforcement learning, which can be understood as an IV regression task. | https://openreview.net/pdf/44b63f5cdbd7a7ace70009826587fc8c176ef370.pdf |
Statistical inference for individual fairness | https://openreview.net/forum?id=z9k8BWL-_2u | https://openreview.net/forum?id=z9k8BWL-_2u | Subha Maity,Songkai Xue,Mikhail Yurochkin,Yuekai Sun | ICLR 2021,Poster | As we rely on machine learning (ML) models to make more consequential decisions, the issue of ML models perpetuating unwanted social biases has come to the fore of the public's and the research community's attention. In this paper, we focus on the problem of detecting violations of individual fairness in ML models. We formalize the problem as measuring the susceptibility of ML models against a form of adversarial attack and develop a suite of inference tools for the adversarial loss. The tools allow practitioners to assess the individual fairness of ML models in a statistically-principled way: form confidence intervals for the adversarial loss and test hypotheses of model fairness with (asymptotic) non-coverage/Type I error rate control. We demonstrate the utility of our tools in a real-world case study. | https://openreview.net/pdf/ac8d0be951fdef76c35e87af88b3fb817cadfde9.pdf |
Randomized Ensembled Double Q-Learning: Learning Fast Without a Model | https://openreview.net/forum?id=AY8zfZm0tDd | https://openreview.net/forum?id=AY8zfZm0tDd | Xinyue Chen,Che Wang,Zijian Zhou,Keith W. Ross | ICLR 2021,Poster | Using a high Update-To-Data (UTD) ratio, model-based methods have recently achieved much higher sample efficiency than previous model-free methods for continuous-action DRL benchmarks. In this paper, we introduce a simple model-free algorithm, Randomized Ensembled Double Q-Learning (REDQ), and show that its performance is just as good as, if not better than, a state-of-the-art model-based algorithm for the MuJoCo benchmark. Moreover, REDQ can achieve this performance using fewer parameters than the model-based method, and with less wall-clock run time. REDQ has three carefully integrated ingredients which allow it to achieve its high performance: (i) a UTD ratio $\gg 1$; (ii) an ensemble of Q functions; (iii) in-target minimization across a random subset of Q functions from the ensemble. Through carefully designed experiments, we provide a detailed analysis of REDQ and related model-free algorithms. To our knowledge, REDQ is the first successful model-free DRL algorithm for continuous-action spaces using a UTD ratio $\gg 1$. | https://openreview.net/pdf/c4e6642e1eb45768ab5d7a3291c2a0088665e5c4.pdf |
Lifelong Learning of Compositional Structures | https://openreview.net/forum?id=ADWd4TJO13G | https://openreview.net/forum?id=ADWd4TJO13G | Jorge A Mendez,ERIC EATON | ICLR 2021,Poster | A hallmark of human intelligence is the ability to construct self-contained chunks of knowledge and adequately reuse them in novel combinations for solving different yet structurally related problems. Learning such compositional structures has been a significant challenge for artificial systems, due to the combinatorial nature of the underlying search problem. To date, research into compositional learning has largely proceeded separately from work on lifelong or continual learning. We integrate these two lines of work to present a general-purpose framework for lifelong learning of compositional structures that can be used for solving a stream of related tasks. Our framework separates the learning process into two broad stages: learning how to best combine existing components in order to assimilate a novel problem, and learning how to adapt the set of existing components to accommodate the new problem. This separation explicitly handles the trade-off between the stability required to remember how to solve earlier tasks and the flexibility required to solve new tasks, as we show empirically in an extensive evaluation. | https://openreview.net/pdf/4098c1ce3205eddbf5c4ba54920410460b0861cb.pdf |
QPLEX: Duplex Dueling Multi-Agent Q-Learning | https://openreview.net/forum?id=Rcmk0xxIQV | https://openreview.net/forum?id=Rcmk0xxIQV | Jianhao Wang,Zhizhou Ren,Terry Liu,Yang Yu,Chongjie Zhang | ICLR 2021,Poster | We explore value-based multi-agent reinforcement learning (MARL) in the popular paradigm of centralized training with decentralized execution (CTDE). CTDE has an important concept, Individual-Global-Max (IGM) principle, which requires the consistency between joint and local action selections to support efficient local decision-making. However, in order to achieve scalability, existing MARL methods either limit representation expressiveness of their value function classes or relax the IGM consistency, which may suffer from instability risk or may not perform well in complex domains. This paper presents a novel MARL approach, called duPLEX dueling multi-agent Q-learning (QPLEX), which takes a duplex dueling network architecture to factorize the joint value function. This duplex dueling structure encodes the IGM principle into the neural network architecture and thus enables efficient value function learning. Theoretical analysis shows that QPLEX achieves a complete IGM function class. Empirical experiments on StarCraft II micromanagement tasks demonstrate that QPLEX significantly outperforms state-of-the-art baselines in both online and offline data collection settings, and also reveal that QPLEX achieves high sample efficiency and can benefit from offline datasets without additional online exploration. | https://openreview.net/pdf/b346f46b6a8ed100f25b8d16fde7d2500965fd70.pdf |
Meta-Learning of Structured Task Distributions in Humans and Machines | https://openreview.net/forum?id=--gvHfE3Xf5 | https://openreview.net/forum?id=--gvHfE3Xf5 | Sreejan Kumar,Ishita Dasgupta,Jonathan Cohen,Nathaniel Daw,Thomas Griffiths | ICLR 2021,Poster | In recent years, meta-learning, in which a model is trained on a family of tasks (i.e. a task distribution), has emerged as an approach to training neural networks to perform tasks that were previously assumed to require structured representations, making strides toward closing the gap between humans and machines. However, we argue that evaluating meta-learning remains a challenge, and can miss whether meta-learning actually uses the structure embedded within the tasks. These meta-learners might therefore still be significantly different from humans learners. To demonstrate this difference, we first define a new meta-reinforcement learning task in which a structured task distribution is generated using a compositional grammar. We then introduce a novel approach to constructing a "null task distribution" with the same statistical complexity as this structured task distribution but without the explicit rule-based structure used to generate the structured task. We train a standard meta-learning agent, a recurrent network trained with model-free reinforcement learning, and compare it with human performance across the two task distributions. We find a double dissociation in which humans do better in the structured task distribution whereas agents do better in the null task distribution -- despite comparable statistical complexity. This work highlights that multiple strategies can achieve reasonable meta-test performance, and that careful construction of control task distributions is a valuable way to understand which strategies meta-learners acquire, and how they might differ from humans. | https://openreview.net/pdf/ead1cbc3c9174506e0aa95f2a8fe5deb881931c1.pdf |
PC2WF: 3D Wireframe Reconstruction from Raw Point Clouds | https://openreview.net/forum?id=8X2eaSZxTP | https://openreview.net/forum?id=8X2eaSZxTP | Yujia Liu,Stefano D'Aronco,Konrad Schindler,Jan Dirk Wegner | ICLR 2021,Poster | We introduce PC2WF, the first end-to-end trainable deep network architecture to convert a 3D point cloud into a wireframe model. The network takes as input an unordered set of 3D points sampled from the surface of some object, and outputs a wireframe of that object, i.e., a sparse set of corner points linked by line segments. Recovering the wireframe is a challenging task, where the numbers of both vertices and edges are different for every instance, and a-priori unknown. Our architecture gradually builds up the model: It starts by encoding the points into feature vectors. Based on those features, it identifies a pool of candidate vertices, then prunes those candidates to a final set of corner vertices and refines their locations. Next, the corners are linked with an exhaustive set of candidate edges, which is again pruned to obtain the final wireframe. All steps are trainable, and errors can be backpropagated through the entire sequence. We validate the proposed model on a publicly available synthetic dataset, for which the ground truth wireframes are accessible, as well as on a new real-world dataset. Our model produces wireframe abstractions of good quality and outperforms several baselines. | https://openreview.net/pdf/3de76ce33a27b6e85c1414aa9fa6edfa54803af0.pdf |
Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks | https://openreview.net/forum?id=9l0K4OM-oXE | https://openreview.net/forum?id=9l0K4OM-oXE | Yige Li,Xixiang Lyu,Nodens Koren,Lingjuan Lyu,Bo Li,Xingjun Ma | ICLR 2021,Poster | Deep neural networks (DNNs) are known vulnerable to backdoor attacks, a training time attack that injects a trigger pattern into a small proportion of training data so as to control the model's prediction at the test time. Backdoor attacks are notably dangerous since they do not affect the model's performance on clean examples, yet can fool the model to make the incorrect prediction whenever the trigger pattern appears during testing. In this paper, we propose a novel defense framework Neural Attention Distillation (NAD) to erase backdoor triggers from backdoored DNNs. NAD utilizes a teacher network to guide the finetuning of the backdoored student network on a small clean subset of data such that the intermediate-layer attention of the student network aligns with that of the teacher network. The teacher network can be obtained by an independent finetuning process on the same clean subset. We empirically show, against 6 state-of-the-art backdoor attacks, NAD can effectively erase the backdoor triggers using only 5\% clean training data without causing obvious performance degradation on clean examples. Our code is available at https://github.com/bboylyg/NAD. | https://openreview.net/pdf/42f5786a622e8cdc4ce43d79d5d83ebe8e4feeeb.pdf |
HyperGrid Transformers: Towards A Single Model for Multiple Tasks | https://openreview.net/forum?id=hiq1rHO8pNT | https://openreview.net/forum?id=hiq1rHO8pNT | Yi Tay,Zhe Zhao,Dara Bahri,Donald Metzler,Da-Cheng Juan | ICLR 2021,Poster | Achieving state-of-the-art performance on natural language understanding tasks typically relies on fine-tuning a fresh model for every task. Consequently, this approach leads to a higher overall parameter cost, along with higher technical maintenance for serving multiple models. Learning a single multi-task model that is able to do well for all the tasks has been a challenging and yet attractive proposition. In this paper, we propose HyperGrid Transformers, a new Transformer architecture that leverages task-conditioned hyper networks for controlling its feed-forward layers. Specifically, we propose a decomposable hypernetwork that learns grid-wise projections that help to specialize regions in weight matrices for different tasks. In order to construct the proposed hypernetwork, our method learns the interactions and composition between a global (task-agnostic) state and a local task-specific state. We conduct an extensive set of experiments on GLUE/SuperGLUE. On the SuperGLUE test set, we match the performance of the state-of-the-art while being $16$ times more parameter efficient. Our method helps bridge the gap between fine-tuning and multi-task learning approaches. | https://openreview.net/pdf/ec11dfff9f173589062f4d3948f9212e345c56cf.pdf |
Long Range Arena : A Benchmark for Efficient Transformers | https://openreview.net/forum?id=qVyeW-grC2k | https://openreview.net/forum?id=qVyeW-grC2k | Yi Tay,Mostafa Dehghani,Samira Abnar,Yikang Shen,Dara Bahri,Philip Pham,Jinfeng Rao,Liu Yang,Sebastian Ruder,Donald Metzler | ICLR 2021,Poster | Transformers do not scale very well to long sequence lengths largely because of quadratic self-attention complexity. In the recent months, a wide spectrum of efficient, fast Transformers have been proposed to tackle this problem, more often than not claiming superior or comparable model quality to vanilla Transformer models. To this date, there is no well-established consensus on how to evaluate this class of models. Moreover, inconsistent benchmarking on a wide spectrum of tasks and datasets makes it difficult to assess relative model quality amongst many models. This paper proposes a systematic and unified benchmark, Long Range Arena, specifically focused on evaluating model quality under long-context scenarios. Our benchmark is a suite of tasks consisting of sequences ranging from $1K$ to $16K$ tokens, encompassing a wide range of data types and modalities such as text, natural, synthetic images, and mathematical expressions requiring similarity, structural, and visual-spatial reasoning. We systematically evaluate ten well-established long-range Transformer models (Reformers, Linformers, Linear Transformers, Sinkhorn Transformers, Performers, Synthesizers, Sparse Transformers, and Longformers) on our newly proposed benchmark suite. Long Range Arena paves the way towards better understanding this class of efficient Transformer models, facilitates more research in this direction, and presents new challenging tasks to tackle. | https://openreview.net/pdf/c7ddcda9fb422b91032d80ebd1564c35dd6f9fa8.pdf |
Acting in Delayed Environments with Non-Stationary Markov Policies | https://openreview.net/forum?id=j1RMMKeP2gR | https://openreview.net/forum?id=j1RMMKeP2gR | Esther Derman,Gal Dalal,Shie Mannor | ICLR 2021,Poster | The standard Markov Decision Process (MDP) formulation hinges on the assumption that an action is executed immediately after it was chosen. However, assuming it is often unrealistic and can lead to catastrophic failures in applications such as robotic manipulation, cloud computing, and finance. We introduce a framework for learning and planning in MDPs where the decision-maker commits actions that are executed with a delay of $m$ steps. The brute-force state augmentation baseline where the state is concatenated to the last $m$ committed actions suffers from an exponential complexity in $m$, as we show for policy iteration. We then prove that with execution delay, deterministic Markov policies in the original state-space are sufficient for attaining maximal reward, but need to be non-stationary. As for stationary Markov policies, we show they are sub-optimal in general. Consequently, we devise a non-stationary Q-learning style model-based algorithm that solves delayed execution tasks without resorting to state-augmentation. Experiments on tabular, physical, and Atari domains reveal that it converges quickly to high performance even for substantial delays, while standard approaches that either ignore the delay or rely on state-augmentation struggle or fail due to divergence. The code is available at \url{https://github.com/galdl/rl_delay_basic.git}. | https://openreview.net/pdf/da00ad1a9fdf6ed49ff470887d9046490c8058b9.pdf |
Neurally Augmented ALISTA | https://openreview.net/forum?id=q_S44KLQ_Aa | https://openreview.net/forum?id=q_S44KLQ_Aa | Freya Behrens,Jonathan Sauder,Peter Jung | ICLR 2021,Poster | It is well-established that many iterative sparse reconstruction algorithms can be unrolled to yield a learnable neural network for improved empirical performance. A prime example is learned ISTA (LISTA) where weights, step sizes and thresholds are learned from training data. Recently, Analytic LISTA (ALISTA) has been introduced, combining the strong empirical performance of a fully learned approach like LISTA, while retaining theoretical guarantees of classical compressed sensing algorithms and significantly reducing the number of parameters to learn. However, these parameters are trained to work in expectation, often leading to suboptimal reconstruction of individual targets. In this work we therefore introduce Neurally Augmented ALISTA, in which an LSTM network is used to compute step sizes and thresholds individually for each target vector during reconstruction. This adaptive approach is theoretically motivated by revisiting the recovery guarantees of ALISTA. We show that our approach further improves empirical performance in sparse reconstruction, in particular outperforming existing algorithms by an increasing margin as the compression ratio becomes more challenging. | https://openreview.net/pdf/47516abe23cbccb7a1e7ed8520cf57ee2f68581f.pdf |
Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models | https://openreview.net/forum?id=vhKe9UFbrJo | https://openreview.net/forum?id=vhKe9UFbrJo | Yuge Shi,Brooks Paige,Philip Torr,Siddharth N | ICLR 2021,Poster | Multimodal learning for generative models often refers to the learning of abstract concepts from the commonality of information in multiple modalities, such as vision and language. While it has proven effective for learning generalisable representations, the training of such models often requires a large amount of related multimodal data that shares commonality, which can be expensive to come by. To mitigate this, we develop a novel contrastive framework for generative model learning, allowing us to train the model not just by the commonality between modalities, but by the distinction between "related" and "unrelated" multimodal data. We show in experiments that our method enables data-efficient multimodal learning on challenging datasets for various multimodal VAE models. We also show that under our proposed framework, the generative model can accurately identify related samples from unrelated ones, making it possible to make use of the plentiful unlabeled, unpaired multimodal data. | https://openreview.net/pdf/9f989526ad09a1935c06a52631aac027c4f90633.pdf |
Categorical Normalizing Flows via Continuous Transformations | https://openreview.net/forum?id=-GLNZeVDuik | https://openreview.net/forum?id=-GLNZeVDuik | Phillip Lippe,Efstratios Gavves | ICLR 2021,Poster | Despite their popularity, to date, the application of normalizing flows on categorical data stays limited. The current practice of using dequantization to map discrete data to a continuous space is inapplicable as categorical data has no intrinsic order. Instead, categorical data have complex and latent relations that must be inferred, like the synonymy between words. In this paper, we investigate Categorical Normalizing Flows, that is normalizing flows for categorical data. By casting the encoding of categorical data in continuous space as a variational inference problem, we jointly optimize the continuous representation and the model likelihood. Using a factorized decoder, we introduce an inductive bias to model any interactions in the normalizing flow. As a consequence, we do not only simplify the optimization compared to having a joint decoder, but also make it possible to scale up to a large number of categories that is currently impossible with discrete normalizing flows. Based on Categorical Normalizing Flows, we propose GraphCNF a permutation-invariant generative model on graphs. GraphCNF implements a three step approach modeling the nodes, edges, and adjacency matrix stepwise to increase efficiency. On molecule generation, GraphCNF outperforms both one-shot and autoregressive flow-based state-of-the-art.
| https://openreview.net/pdf/ce39823e0ee0cf47b03da3ce20b4927562d5b1b5.pdf |
Prototypical Representation Learning for Relation Extraction | https://openreview.net/forum?id=aCgLmfhIy_f | https://openreview.net/forum?id=aCgLmfhIy_f | Ning Ding,Xiaobin Wang,Yao Fu,Guangwei Xu,Rui Wang,Pengjun Xie,Ying Shen,Fei Huang,Hai-Tao Zheng,Rui Zhang | ICLR 2021,Poster | Recognizing relations between entities is a pivotal task of relational learning.
Learning relation representations from distantly-labeled datasets is difficult because of the abundant label noise and complicated expressions in human language.
This paper aims to learn predictive, interpretable, and robust relation representations from distantly-labeled data that are effective in different settings, including supervised, distantly supervised, and few-shot learning.
Instead of solely relying on the supervision from noisy labels, we propose to learn prototypes for each relation from contextual information to best explore the intrinsic semantics of relations.
Prototypes are representations in the feature space abstracting the essential semantics of relations between entities in sentences.
We learn prototypes based on objectives with clear geometric interpretation, where the prototypes are unit vectors uniformly dispersed in a unit ball, and statement embeddings are centered at the end of their corresponding prototype vectors on the surface of the ball.
This approach allows us to learn meaningful, interpretable prototypes for the final classification.
Results on several relation learning tasks show that our model significantly outperforms the previous state-of-the-art models.
We further demonstrate the robustness of the encoder and the interpretability of prototypes with extensive experiments. | https://openreview.net/pdf/9312e6620fd3e8d85f647ce019d116125060d87d.pdf |
Generalized Energy Based Models | https://openreview.net/forum?id=0PtUPB9z6qK | https://openreview.net/forum?id=0PtUPB9z6qK | Michael Arbel,Liang Zhou,Arthur Gretton | ICLR 2021,Poster | We introduce the Generalized Energy Based Model (GEBM) for generative modelling. These models combine two trained components: a base distribution (generally an implicit model), which can learn the support of data with low intrinsic dimension in a high dimensional space; and an energy function, to refine the probability mass on the learned support.
Both the energy function and base jointly constitute the final model, unlike GANs, which retain only the base distribution (the "generator").
GEBMs are trained by alternating between learning the energy and the base.
We show that both training stages are well-defined: the energy is learned by maximising a generalized likelihood, and the resulting energy-based loss provides informative gradients for learning the base.
Samples from the posterior on the latent space of the trained model can be obtained via MCMC, thus finding regions in this space that produce better quality samples.
Empirically, the GEBM samples on image-generation tasks are of much better quality than those from the learned generator alone, indicating that all else being equal, the GEBM will outperform a GAN of the same complexity. When using normalizing flows as base measures, GEBMs succeed on density modelling tasks returning comparable performance to direct maximum likelihood of the same networks. | https://openreview.net/pdf/57e4351773a6685efefb862344e527a30edf072c.pdf |
Predicting Classification Accuracy When Adding New Unobserved Classes | https://openreview.net/forum?id=Y9McSeEaqUh | https://openreview.net/forum?id=Y9McSeEaqUh | Yuli Slavutsky,Yuval Benjamini | ICLR 2021,Poster | Multiclass classifiers are often designed and evaluated only on a sample from the classes on which they will eventually be applied. Hence, their final accuracy remains unknown. In this work we study how a classifier’s performance over the initial class sample can be used to extrapolate its expected accuracy on a larger, unobserved set of classes. For this, we define a measure of separation between correct and incorrect classes that is independent of the number of classes: the "reversed ROC" (rROC), which is obtained by replacing the roles of classes and data-points in the common ROC. We show that the classification accuracy is a function of the rROC in multiclass classifiers, for which the learned representation of data from the initial class sample remains unchanged when new classes are added. Using these results we formulate a robust neural-network-based algorithm, "CleaneX", which learns to estimate the accuracy of such classifiers on arbitrarily large sets of classes. Unlike previous methods, our method uses both the observed accuracies of the classifier and densities of classification scores, and therefore achieves remarkably better predictions than current state-of-the-art methods on both simulations and real datasets of object detection, face recognition, and brain decoding. | https://openreview.net/pdf/0a83be3c3bae29a302683c1ec66eeea1b7307703.pdf |
In Search of Lost Domain Generalization | https://openreview.net/forum?id=lQdXeXDoWtI | https://openreview.net/forum?id=lQdXeXDoWtI | Ishaan Gulrajani,David Lopez-Paz | ICLR 2021,Poster | The goal of domain generalization algorithms is to predict well on distributions different from those seen during training.
While a myriad of domain generalization algorithms exist, inconsistencies in experimental conditions---datasets, network architectures, and model selection criteria---render fair comparisons difficult.
The goal of this paper is to understand how useful domain generalization algorithms are in realistic settings.
As a first step, we realize that model selection is non-trivial for domain generalization tasks, and we argue that algorithms without a model selection criterion remain incomplete.
Next we implement DomainBed, a testbed for domain generalization including seven benchmarks, fourteen algorithms, and three model selection criteria.
When conducting extensive experiments using DomainBed we find that when carefully implemented and tuned, ERM outperforms the state-of-the-art in terms of average performance.
Furthermore, no algorithm included in DomainBed outperforms ERM by more than one point when evaluated under the same experimental conditions.
We hope that the release of DomainBed, alongside contributions from fellow researchers, will streamline reproducible and rigorous advances in domain generalization. | https://openreview.net/pdf/83e32f2664eddb15fdc2ac88f7bff07dd2682647.pdf |
Estimating informativeness of samples with Smooth Unique Information | https://openreview.net/forum?id=kEnBH98BGs5 | https://openreview.net/forum?id=kEnBH98BGs5 | Hrayr Harutyunyan,Alessandro Achille,Giovanni Paolini,Orchid Majumder,Avinash Ravichandran,Rahul Bhotika,Stefano Soatto | ICLR 2021,Poster | We define a notion of information that an individual sample provides to the training of a neural network, and we specialize it to measure both how much a sample informs the final weights and how much it informs the function computed by the weights. Though related, we show that these quantities have a qualitatively different behavior. We give efficient approximations of these quantities using a linearized network and demonstrate empirically that the approximation is accurate for real-world architectures, such as pre-trained ResNets. We apply these measures to several problems, such as dataset summarization, analysis of under-sampled classes, comparison of informativeness of different data sources, and detection of adversarial and corrupted examples. Our work generalizes existing frameworks, but enjoys better computational properties for heavily over-parametrized models, which makes it possible to apply it to real-world networks. | https://openreview.net/pdf/95f6e5e49f7926d28ebfda829ba602f38fd56084.pdf |
Identifying Physical Law of Hamiltonian Systems via Meta-Learning | https://openreview.net/forum?id=45NZvF1UHam | https://openreview.net/forum?id=45NZvF1UHam | Seungjun Lee,Haesang Yang,Woojae Seong | ICLR 2021,Poster | Hamiltonian mechanics is an effective tool to represent many physical processes with concise yet well-generalized mathematical expressions. A well-modeled Hamiltonian makes it easy for researchers to analyze and forecast many related phenomena that are governed by the same physical law. However, in general, identifying a functional or shared expression of the Hamiltonian is very difficult. It requires carefully designed experiments and the researcher's insight that comes from years of experience. We propose that meta-learning algorithms can be potentially powerful data-driven tools for identifying the physical law governing Hamiltonian systems without any mathematical assumptions on the representation, but with observations from a set of systems governed by the same physical law. We show that a well meta-trained learner can identify the shared representation of the Hamiltonian by evaluating our method on several types of physical systems with various experimental settings. | https://openreview.net/pdf/258e47b1950abbe8b7433ead4dbc6ec6b2b0a62a.pdf |
Adapting to Reward Progressivity via Spectral Reinforcement Learning | https://openreview.net/forum?id=dyjPVUc2KB | https://openreview.net/forum?id=dyjPVUc2KB | Michael Dann,John Thangarajah | ICLR 2021,Poster | In this paper we consider reinforcement learning tasks with progressive rewards; that is, tasks where the rewards tend to increase in magnitude over time. We hypothesise that this property may be problematic for value-based deep reinforcement learning agents, particularly if the agent must first succeed in relatively unrewarding regions of the task in order to reach more rewarding regions. To address this issue, we propose Spectral DQN, which decomposes the reward into frequencies such that the high frequencies only activate when large rewards are found. This allows the training loss to be balanced so that it gives more even weighting across small and large reward regions. In two domains with extreme reward progressivity, where standard value-based methods struggle significantly, Spectral DQN is able to make much farther progress. Moreover, when evaluated on a set of six standard Atari games that do not overtly favour the approach, Spectral DQN remains more than competitive: While it underperforms one of the benchmarks in a single game, it comfortably surpasses the benchmarks in three games. These results demonstrate that the approach is not overfit to its target problem, and suggest that Spectral DQN may have advantages beyond addressing reward progressivity. | https://openreview.net/pdf/a3879d188d633661f6593af48f3c561aefbfb6ff.pdf |
Understanding the effects of data parallelism and sparsity on neural network training | https://openreview.net/forum?id=rsogjAnYs4z | https://openreview.net/forum?id=rsogjAnYs4z | Namhoon Lee,Thalaiyasingam Ajanthan,Philip Torr,Martin Jaggi | ICLR 2021,Poster | We study two factors in neural network training: data parallelism and sparsity; here, data parallelism means processing training data in parallel using distributed systems (or equivalently increasing batch size), so that training can be accelerated; for sparsity, we refer to pruning parameters in a neural network model, so as to reduce computational and memory cost. Despite their promising benefits, however, understanding of their effects on neural network training remains elusive. In this work, we first measure these effects rigorously by conducting extensive experiments while tuning all metaparameters involved in the optimization. As a result, we find across various workloads of data set, network model, and optimization algorithm that there exists a general scaling trend between batch size and number of training steps to convergence for the effect of data parallelism, and further, difficulty of training under sparsity. Then, we develop a theoretical analysis based on the convergence properties of stochastic gradient methods and smoothness of the optimization landscape, which illustrates the observed phenomena precisely and generally, establishing a better account of the effects of data parallelism and sparsity on neural network training. | https://openreview.net/pdf/ccc4046bb2f3bad4efbb7f58fb7f860fa43b7d4d.pdf |
Primal Wasserstein Imitation Learning | https://openreview.net/forum?id=TtYSU29zgR | https://openreview.net/forum?id=TtYSU29zgR | Robert Dadashi,Leonard Hussenot,Matthieu Geist,Olivier Pietquin | ICLR 2021,Poster | Imitation Learning (IL) methods seek to match the behavior of an agent with that of an expert. In the present work, we propose a new IL method based on a conceptually simple algorithm: Primal Wasserstein Imitation Learning (PWIL), which ties to the primal form of the Wasserstein distance between the expert and the agent state-action distributions. We present a reward function which is derived offline, as opposed to recent adversarial IL algorithms that learn a reward function through interactions with the environment, and which requires little fine-tuning. We show that we can recover expert behavior on a variety of continuous control tasks of the MuJoCo domain in a sample efficient manner in terms of agent interactions and of expert interactions with the environment. Finally, we show that the behavior of the agent we train matches the behavior of the expert with the Wasserstein distance, rather than the commonly used proxy of performance. | https://openreview.net/pdf/a0cf4ec3c4a75e202ba613caaea4f173d8e53101.pdf |
Prediction and generalisation over directed actions by grid cells | https://openreview.net/forum?id=Ptaz_zIFbX | https://openreview.net/forum?id=Ptaz_zIFbX | Changmin Yu,Timothy Behrens,Neil Burgess | ICLR 2021,Poster | Knowing how the effects of directed actions generalise to new situations (e.g. moving North, South, East and West, or turning left, right, etc.) is key to rapid generalisation across new situations. Markovian tasks can be characterised by a state space and a transition matrix and recent work has proposed that neural grid codes provide an efficient representation of the state space, as eigenvectors of a transition matrix reflecting diffusion across states, that allows efficient prediction of future state distributions. Here we extend the eigenbasis prediction model, utilising tools from Fourier analysis, to prediction over arbitrary translation-invariant directed transition structures (i.e. displacement and diffusion), showing that a single set of eigenvectors can support predictions over arbitrary directed actions via action-specific eigenvalues. We show how to define a "sense of direction" to combine actions to reach a target state (ignoring task-specific deviations from translation-invariance), and demonstrate that adding the Fourier representations to a deep Q network aids policy learning in continuous control tasks. We show the equivalence between the generalised prediction framework and traditional models of grid cell firing driven by self-motion to perform path integration, either using oscillatory interference (via Fourier components as velocity-controlled oscillators) or continuous attractor networks (via analysis of the update dynamics). We thus provide a unifying framework for the role of the grid system in predictive planning, sense of direction and path integration: supporting generalisable inference over directed actions across different tasks. | https://openreview.net/pdf/708e623b918bb762d8cc7249aa89623b0dad19d6.pdf |
Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration | https://openreview.net/forum?id=1rxHOBjeDUW | https://openreview.net/forum?id=1rxHOBjeDUW | Jaekyeom Kim,Minjung Kim,Dongyeon Woo,Gunhee Kim | ICLR 2021,Poster | We propose a novel information bottleneck (IB) method named Drop-Bottleneck, which discretely drops features that are irrelevant to the target variable. Drop-Bottleneck not only enjoys a simple and tractable compression objective but also additionally provides a deterministic compressed representation of the input variable, which is useful for inference tasks that require consistent representation. Moreover, it can jointly learn a feature extractor and select features considering each feature dimension's relevance to the target task, which is unattainable by most neural network-based IB methods. We propose an exploration method based on Drop-Bottleneck for reinforcement learning tasks. In a multitude of noisy and reward sparse maze navigation tasks in VizDoom (Kempka et al., 2016) and DMLab (Beattie et al., 2016), our exploration method achieves state-of-the-art performance. As a new IB framework, we demonstrate that Drop-Bottleneck outperforms Variational Information Bottleneck (VIB) (Alemi et al., 2017) in multiple aspects including adversarial robustness and dimensionality reduction. | https://openreview.net/pdf/c63f2b34fa1ef6e970ac9be2d872af6673e0d88a.pdf |
BOIL: Towards Representation Change for Few-shot Learning | https://openreview.net/forum?id=umIdUL8rMH | https://openreview.net/forum?id=umIdUL8rMH | Jaehoon Oh,Hyungjun Yoo,ChangHwan Kim,Se-Young Yun | ICLR 2021,Poster | Model Agnostic Meta-Learning (MAML) is one of the most representative of gradient-based meta-learning algorithms. MAML learns new tasks with a few data samples using inner updates from a meta-initialization point and learns the meta-initialization parameters with outer updates. It has recently been hypothesized that representation reuse, which makes little change in efficient representations, is the dominant factor in the performance of the meta-initialized model through MAML in contrast to representation change, which causes a significant change in representations. In this study, we investigate the necessity of representation change for the ultimate goal of few-shot learning, which is solving domain-agnostic tasks. To this aim, we propose a novel meta-learning algorithm, called BOIL (Body Only update in Inner Loop), which updates only the body (extractor) of the model and freezes the head (classifier) during inner loop updates. BOIL leverages representation change rather than representation reuse. A frozen head cannot achieve better results than even a random guessing classifier at the initial point of new tasks, and feature vectors (representations) have to move quickly to their corresponding frozen head vectors. We visualize this property using cosine similarity, CKA, and empirical results without the head. Although the inner loop updates purely hinge on representation change, BOIL empirically shows significant performance improvement over MAML, particularly on cross-domain tasks. The results imply that representation change in gradient-based meta-learning approaches is a critical component. | https://openreview.net/pdf/1a8c0d92bdde5a60d7b354b43c36e22abacfc99a.pdf |
MultiModalQA: complex question answering over text, tables and images | https://openreview.net/forum?id=ee6W5UgQLa | https://openreview.net/forum?id=ee6W5UgQLa | Alon Talmor,Ori Yoran,Amnon Catav,Dan Lahav,Yizhong Wang,Akari Asai,Gabriel Ilharco,Hannaneh Hajishirzi,Jonathan Berant | ICLR 2021,Poster | When answering complex questions, people can seamlessly combine information from visual, textual and tabular sources.
While interest in models that reason over multiple pieces of evidence has surged in recent years, there has been relatively little work on question answering models that reason across multiple modalities.
In this paper, we present MultiModalQA (MMQA): a challenging question answering dataset that requires joint reasoning over text, tables and images.
We create MMQA using a new framework for generating complex multi-modal questions at scale, harvesting tables from Wikipedia, and attaching images and text paragraphs using entities that appear in each table. We then define a formal language that allows us to take questions that can be answered from a single modality, and combine them to generate cross-modal questions. Last, crowdsourcing workers take these automatically generated questions and rephrase them into more fluent language.
We create 29,918 questions through this procedure, and empirically demonstrate the necessity of a multi-modal multi-hop approach to solve our task: our multi-hop model, ImplicitDecomp, achieves an average F1 of 51.7 over cross-modal questions, substantially outperforming a strong baseline that achieves 38.2 F1, but still lags significantly behind human performance, which is at 90.1 F1. | https://openreview.net/pdf/f3dad930cb55abce99a229e35cc131a2db791b66.pdf |
Neural gradients are near-lognormal: improved quantized and sparse training | https://openreview.net/forum?id=EoFNy62JGd | https://openreview.net/forum?id=EoFNy62JGd | Brian Chmiel,Liad Ben-Uri,Moran Shkolnik,Elad Hoffer,Ron Banner,Daniel Soudry | ICLR 2021,Poster | While training can mostly be accelerated by reducing the time needed to propagate neural gradients (loss gradients with respect to the intermediate neural layer outputs) back throughout the model, most previous works focus on the quantization/pruning of weights and activations. These methods are often not applicable to neural gradients, which have very different statistical properties. Distinguished from weights and activations, we find that the distribution of neural gradients is approximately lognormal. Considering this, we suggest two closed-form analytical methods to reduce the computational and memory burdens of neural gradients. The first method optimizes the floating-point format and scale of the gradients. The second method accurately sets sparsity thresholds for gradient pruning. Each method achieves state-of-the-art results on ImageNet. To the best of our knowledge, this paper is the first to (1) quantize the gradients to 6-bit floating-point formats, or (2) achieve up to 85% gradient sparsity --- in each case without accuracy degradation.
Reference implementation accompanies the paper in the supplementary material. | https://openreview.net/pdf/6931f8bb8366bb42bd29d92eb0e841b8ac24f29e.pdf |
Group Equivariant Conditional Neural Processes | https://openreview.net/forum?id=e8W-hsu_q5 | https://openreview.net/forum?id=e8W-hsu_q5 | Makoto Kawano,Wataru Kumagai,Akiyoshi Sannai,Yusuke Iwasawa,Yutaka Matsuo | ICLR 2021,Poster | We present the group equivariant conditional neural process (EquivCNP), a meta-learning method with permutation invariance in a data set as in conventional conditional neural processes (CNPs), and it also has transformation equivariance in data space. Incorporating group equivariance, such as rotation and scaling equivariance, provides a way to consider the symmetry of real-world data. We give a decomposition theorem for permutation-invariant and group-equivariant maps, which leads us to construct EquivCNPs with an infinite-dimensional latent space to handle group symmetries. In this paper, we build architecture using Lie group convolutional layers for practical implementation. We show that EquivCNP with translation equivariance achieves comparable performance to conventional CNPs in a 1D regression task. Moreover, we demonstrate that incorporating an appropriate Lie group equivariance, EquivCNP is capable of zero-shot generalization for an image-completion task by selecting an appropriate Lie group equivariance. | https://openreview.net/pdf/97ca04674c54635beb05af46402f012365bc8226.pdf |
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization | https://openreview.net/forum?id=3hGNqpI4WS | https://openreview.net/forum?id=3hGNqpI4WS | Tatsuya Matsushima,Hiroki Furuta,Yutaka Matsuo,Ofir Nachum,Shixiang Gu | ICLR 2021,Poster | Most reinforcement learning (RL) algorithms assume online access to the environment, in which one may readily interleave updates to the policy with experience collection using that policy. However, in many real-world applications such as health, education, dialogue agents, and robotics, the cost or potential risk of deploying a new data-collection policy is high, to the point that it can become prohibitive to update the data-collection policy more than a few times during learning. With this view, we propose a novel concept of deployment efficiency, measuring the number of distinct data-collection policies that are used during policy learning. We observe that naïvely applying existing model-free offline RL algorithms recursively does not lead to a practical deployment-efficient and sample-efficient algorithm. We propose a novel model-based algorithm, Behavior-Regularized Model-ENsemble (BREMEN), that not only performs better than or comparably as the state-of-the-art dynamic-programming-based and concurrently-proposed model-based offline approaches on existing benchmarks, but can also effectively optimize a policy offline using 10-20 times fewer data than prior works. Furthermore, the recursive application of BREMEN achieves impressive deployment efficiency while maintaining the same or better sample efficiency, learning successful policies from scratch on simulated robotic environments with only 5-10 deployments, compared to typical values of hundreds to millions in standard RL baselines. | https://openreview.net/pdf/b73e1d0a56094306542cc2020c438a4e4942e58f.pdf |
Efficient Conformal Prediction via Cascaded Inference with Expanded Admission | https://openreview.net/forum?id=tnSo6VRLmT | https://openreview.net/forum?id=tnSo6VRLmT | Adam Fisch,Tal Schuster,Tommi S. Jaakkola,Regina Barzilay | ICLR 2021,Poster | 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. | https://openreview.net/pdf/0fc460673e85f2ec1199b8ea609938585d051465.pdf |
Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks | https://openreview.net/forum?id=sTeoJiB4uR | https://openreview.net/forum?id=sTeoJiB4uR | Thomas Bird,Friso Kingma,David Barber | ICLR 2021,Poster | Deep generative models provide a powerful set of tools to understand real-world data. But as these models improve, they increase in size and complexity, so their computational cost in memory and execution time grows. Using binary weights in neural networks is one method which has shown promise in reducing this cost. However, whether binary neural networks can be used in generative models is an open problem. In this work we show, for the first time, that we can successfully train generative models which utilize binary neural networks. This reduces the computational cost of the models massively. We develop a new class of binary weight normalization, and provide insights for architecture designs of these binarized generative models. We demonstrate that two state-of-the-art deep generative models, the ResNet VAE and Flow++ models, can be binarized effectively using these techniques. We train binary models that achieve loss values close to those of the regular models but are 90%-94% smaller in size, and also allow significant speed-ups in execution time. | https://openreview.net/pdf/4bc920fb592820a6d9fba3d58e93f6561f456f66.pdf |
Large-width functional asymptotics for deep Gaussian neural networks | https://openreview.net/forum?id=0aW6lYOYB7d | https://openreview.net/forum?id=0aW6lYOYB7d | Daniele Bracale,Stefano Favaro,Sandra Fortini,Stefano Peluchetti | ICLR 2021,Poster | In this paper, we consider fully connected feed-forward deep neural networks where weights and biases are independent and identically distributed according to Gaussian distributions. Extending previous results (Matthews et al., 2018a;b;Yang, 2019) we adopt a function-space perspective, i.e. we look at neural networks as infinite-dimensional random elements on the input space $\mathbb{R}^I$. Under suitable assumptions on the activation function we show that: i) a network defines a continuous Gaussian process on the input space $\mathbb{R}^I$; ii) a network with re-scaled weights converges weakly to a continuous Gaussian process in the large-width limit; iii) the limiting Gaussian process has almost surely locally $\gamma$-Hölder continuous paths, for $0 < \gamma <1$. Our results contribute to recent theoretical studies on the interplay between infinitely wide deep neural networks and Gaussian processes by establishing weak convergence in function-space with respect to a stronger metric. | https://openreview.net/pdf/f23196cbe1af7816b29e5ef4110170972165c6ad.pdf |
MetaNorm: Learning to Normalize Few-Shot Batches Across Domains | https://openreview.net/forum?id=9z_dNsC4B5t | https://openreview.net/forum?id=9z_dNsC4B5t | Yingjun Du,Xiantong Zhen,Ling Shao,Cees G. M. Snoek | ICLR 2021,Poster | Batch normalization plays a crucial role when training deep neural networks. However, batch statistics become unstable with small batch sizes and are unreliable in the presence of distribution shifts. We propose MetaNorm, a simple yet effective meta-learning normalization. It tackles the aforementioned issues in a unified way by leveraging the meta-learning setting and learns to infer adaptive statistics for batch normalization. MetaNorm is generic, flexible and model-agnostic, making it a simple plug-and-play module that is seamlessly embedded into existing meta-learning approaches. It can be efficiently implemented by lightweight hypernetworks with low computational cost. We verify its effectiveness by extensive evaluation on representative tasks suffering from the small batch and domain shift problems: few-shot learning and domain generalization. We further introduce an even more challenging setting: few-shot domain generalization. Results demonstrate that MetaNorm consistently achieves better, or at least competitive, accuracy compared to existing batch normalization methods. | https://openreview.net/pdf/e4941f0cbfea169f5f0e844813dd291b73fe78ba.pdf |
Representation Balancing Offline Model-based Reinforcement Learning | https://openreview.net/forum?id=QpNz8r_Ri2Y | https://openreview.net/forum?id=QpNz8r_Ri2Y | Byung-Jun Lee,Jongmin Lee,Kee-Eung Kim | ICLR 2021,Poster | One of the main challenges in offline and off-policy reinforcement learning is to cope with the distribution shift that arises from the mismatch between the target policy and the data collection policy. In this paper, we focus on a model-based approach, particularly on learning the representation for a robust model of the environment under the distribution shift, which has been first studied by Representation Balancing MDP (RepBM). Although this prior work has shown promising results, there are a number of shortcomings that still hinder its applicability to practical tasks. In particular, we address the curse of horizon exhibited by RepBM, rejecting most of the pre-collected data in long-term tasks. We present a new objective for model learning motivated by recent advances in the estimation of stationary distribution corrections. This effectively overcomes the aforementioned limitation of RepBM, as well as naturally extending to continuous action spaces and stochastic policies. We also present an offline model-based policy optimization using this new objective, yielding the state-of-the-art performance in a representative set of benchmark offline RL tasks. | https://openreview.net/pdf/bb8bd2e330b957759e7d56f7715bdb54a256caab.pdf |
Local Convergence Analysis of Gradient Descent Ascent with Finite Timescale Separation | https://openreview.net/forum?id=AWOSz_mMAPx | https://openreview.net/forum?id=AWOSz_mMAPx | Tanner Fiez,Lillian J Ratliff | ICLR 2021,Poster | We study the role that a finite timescale separation parameter $\tau$ has on gradient descent-ascent in non-convex, non-concave zero-sum games where the learning rate of player 1 is denoted by $\gamma_1$ and the learning rate of player 2 is defined to be $\gamma_2=\tau\gamma_1$. We provide a non-asymptotic construction of the finite timescale separation parameter $\tau^{\ast}$ such that gradient descent-ascent locally converges to $x^{\ast}$ for all $\tau \in (\tau^{\ast}, \infty)$ if and only if it is a strict local minmax equilibrium. Moreover, we provide explicit local convergence rates given the finite timescale separation. The convergence results we present are complemented by a non-convergence result: given a critical point $x^{\ast}$ that is not a strict local minmax equilibrium, we present a non-asymptotic construction of a finite timescale separation $\tau_{0}$ such that gradient descent-ascent with timescale separation $\tau\in (\tau_0, \infty)$ does not converge to $x^{\ast}$. Finally, we extend the results to gradient penalty regularization methods for generative adversarial networks and empirically demonstrate on CIFAR-10 and CelebA the significant impact timescale separation has on training performance. | https://openreview.net/pdf/f5f6da5642532f0e8370b55e9965ca3ee0dcc3c1.pdf |
The Importance of Pessimism in Fixed-Dataset Policy Optimization | https://openreview.net/forum?id=E3Ys6a1NTGT | https://openreview.net/forum?id=E3Ys6a1NTGT | Jacob Buckman,Carles Gelada,Marc G Bellemare | ICLR 2021,Poster | We study worst-case guarantees on the expected return of fixed-dataset policy optimization algorithms. Our core contribution is a unified conceptual and mathematical framework for the study of algorithms in this regime. This analysis reveals that for naive approaches, the possibility of erroneous value overestimation leads to a difficult-to-satisfy requirement: in order to guarantee that we select a policy which is near-optimal, we may need the dataset to be informative of the value of every policy. To avoid this, algorithms can follow the pessimism principle, which states that we should choose the policy which acts optimally in the worst possible world. We show why pessimistic algorithms can achieve good performance even when the dataset is not informative of every policy, and derive families of algorithms which follow this principle. These theoretical findings are validated by experiments on a tabular gridworld, and deep learning experiments on four MinAtar environments. | https://openreview.net/pdf/e3dfbddf4c9078c3d334b54122b25fd90aba6c9b.pdf |
Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs | https://openreview.net/forum?id=9EKHN1jOlA | https://openreview.net/forum?id=9EKHN1jOlA | Cheng Wang,Carolin Lawrence,Mathias Niepert | ICLR 2021,Poster | Uncertainty quantification is crucial for building reliable and trustable machine learning systems. We propose to estimate uncertainty in recurrent neural networks (RNNs) via stochastic discrete state transitions over recurrent timesteps. The uncertainty of the model can be quantified by running a prediction several times, each time sampling from the recurrent state transition distribution, leading to potentially different results if the model is uncertain. Alongside uncertainty quantification, our proposed method offers several advantages in different settings. The proposed method can (1) learn deterministic and probabilistic automata from data, (2) learn well-calibrated models on real-world classification tasks, (3) improve the performance of out-of-distribution detection, and (4) control the exploration-exploitation trade-off in reinforcement learning. An implementation is available. | https://openreview.net/pdf/aeebe69f6abcb7b8384fa76d96314140b0529728.pdf |
Empirical or Invariant Risk Minimization? A Sample Complexity Perspective | https://openreview.net/forum?id=jrA5GAccy_ | https://openreview.net/forum?id=jrA5GAccy_ | Kartik Ahuja,Jun Wang,Amit Dhurandhar,Karthikeyan Shanmugam,Kush R. Varshney | ICLR 2021,Poster | Recently, invariant risk minimization (IRM) was proposed as a promising solution to address out-of-distribution (OOD) generalization. However, it is unclear when IRM should be preferred over the widely-employed empirical risk minimization (ERM) framework. In this work, we analyze both these frameworks from the perspective of sample complexity, thus taking a firm step towards answering this important question. We find that depending on the type of data generation mechanism, the two approaches might have very different finite sample and asymptotic behavior. For example, in the covariate shift setting we see that the two approaches not only arrive at the same asymptotic solution, but also have similar finite sample behavior with no clear winner. For other distribution shifts such as those involving confounders or anti-causal variables, however, the two approaches arrive at different asymptotic solutions where IRM is guaranteed to be close to the desired OOD solutions in the finite sample regime, while ERM is biased even asymptotically. We further investigate how different factors --- the number of environments, complexity of the model, and IRM penalty weight --- impact the sample complexity of IRM in relation to its distance from the OOD solutions. | https://openreview.net/pdf/276b48d5dd233a104c8c4d2cffbcf0e687e83747.pdf |
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval | https://openreview.net/forum?id=zeFrfgyZln | https://openreview.net/forum?id=zeFrfgyZln | Lee Xiong,Chenyan Xiong,Ye Li,Kwok-Fung Tang,Jialin Liu,Paul N. Bennett,Junaid Ahmed,Arnold Overwijk | ICLR 2021,Poster | Conducting text retrieval in a learned dense representation space has many intriguing advantages. Yet dense retrieval (DR) often underperforms word-based sparse retrieval. In this paper, we first theoretically show the bottleneck of dense retrieval is the domination of uninformative negatives sampled in mini-batch training, which yield diminishing gradient norms, large gradient variances, and slow convergence. We then propose Approximate nearest neighbor Negative Contrastive Learning (ANCE), which selects hard training negatives globally from the entire corpus. Our experiments demonstrate the effectiveness of ANCE on web search, question answering, and in a commercial search engine, showing ANCE dot-product retrieval nearly matches the accuracy of BERT-based cascade IR pipeline. We also empirically validate our theory that negative sampling with ANCE better approximates the oracle importance sampling procedure and improves learning convergence. | https://openreview.net/pdf/3d6e8b19a81ee8a099bf23bd20b97edafef537b0.pdf |
FairBatch: Batch Selection for Model Fairness | https://openreview.net/forum?id=YNnpaAKeCfx | https://openreview.net/forum?id=YNnpaAKeCfx | Yuji Roh,Kangwook Lee,Steven Euijong Whang,Changho Suh | ICLR 2021,Poster | Training a fair machine learning model is essential to prevent demographic disparity. Existing techniques for improving model fairness require broad changes in either data preprocessing or model training, rendering themselves difficult-to-adopt for potentially already complex machine learning systems. We address this problem via the lens of bilevel optimization. While keeping the standard training algorithm as an inner optimizer, we incorporate an outer optimizer so as to equip the inner problem with an additional functionality: Adaptively selecting minibatch sizes for the purpose of improving model fairness. Our batch selection algorithm, which we call FairBatch, implements this optimization and supports prominent fairness measures: equal opportunity, equalized odds, and demographic parity. FairBatch comes with a significant implementation benefit -- it does not require any modification to data preprocessing or model training. For instance, a single-line change of PyTorch code for replacing batch selection part of model training suffices to employ FairBatch. Our experiments conducted both on synthetic and benchmark real data demonstrate that FairBatch can provide such functionalities while achieving comparable (or even greater) performances against the state of the arts. Furthermore, FairBatch can readily improve fairness of any pre-trained model simply via fine-tuning. It is also compatible with existing batch selection techniques intended for different purposes, such as faster convergence, thus gracefully achieving multiple purposes. | https://openreview.net/pdf/4bc505128a9613d24b343187a2e811fc6f1c05d7.pdf |
An Unsupervised Deep Learning Approach for Real-World Image Denoising | https://openreview.net/forum?id=tIjRAiFmU3y | https://openreview.net/forum?id=tIjRAiFmU3y | Dihan Zheng,Sia Huat Tan,Xiaowen Zhang,Zuoqiang Shi,Kaisheng Ma,Chenglong Bao | ICLR 2021,Poster | Designing an unsupervised image denoising approach in practical applications is a challenging task due to the complicated data acquisition process. In the real-world case, the noise distribution is so complex that the simplified additive white Gaussian (AWGN) assumption rarely holds, which significantly deteriorates the Gaussian denoisers' performance. To address this problem, we apply a deep neural network that maps the noisy image into a latent space in which the AWGN assumption holds, and thus any existing Gaussian denoiser is applicable. More specifically, the proposed neural network consists of the encoder-decoder structure and approximates the likelihood term in the Bayesian framework. Together with a Gaussian denoiser, the neural network can be trained with the input image itself and does not require any pre-training in other datasets. Extensive experiments on real-world noisy image datasets have shown that the combination of neural networks and Gaussian denoisers improves the performance of the original Gaussian denoisers by a large margin. In particular, the neural network+BM3D method significantly outperforms other unsupervised denoising approaches and is competitive with supervised networks such as DnCNN, FFDNet, and CBDNet. | https://openreview.net/pdf/919850dce0c7398051264d88fe81de3a6c0d82f4.pdf |
When Optimizing $f$-Divergence is Robust with Label Noise | https://openreview.net/forum?id=WesiCoRVQ15 | https://openreview.net/forum?id=WesiCoRVQ15 | Jiaheng Wei,Yang Liu | ICLR 2021,Poster | We show when maximizing a properly defined $f$-divergence measure with respect to a classifier's predictions and the supervised labels is robust with label noise. Leveraging its variational form, we derive a nice decoupling property for a family of $f$-divergence measures when label noise presents, where the divergence is shown to be a linear combination of the variational difference defined on the clean distribution and a bias term introduced due to the noise. The above derivation helps us analyze the robustness of different $f$-divergence functions. With established robustness, this family of $f$-divergence functions arises as useful metrics for the problem of learning with noisy labels, which do not require the specification of the labels' noise rate. When they are possibly not robust, we propose fixes to make them so. In addition to the analytical results, we present thorough experimental evidence. Our code is available at https://github.com/UCSC-REAL/Robust-f-divergence-measures. | https://openreview.net/pdf/74da9f07c5ba5a2355f050ac33cbbaa462c63e3d.pdf |
Meta-learning Symmetries by Reparameterization | https://openreview.net/forum?id=-QxT4mJdijq | https://openreview.net/forum?id=-QxT4mJdijq | Allan Zhou,Tom Knowles,Chelsea Finn | ICLR 2021,Poster | Many successful deep learning architectures are equivariant to certain transformations in order to conserve parameters and improve generalization: most famously, convolution layers are equivariant to shifts of the input. This approach only works when practitioners know the symmetries of the task and can manually construct an architecture with the corresponding equivariances. Our goal is an approach for learning equivariances from data, without needing to design custom task-specific architectures. We present a method for learning and encoding equivariances into networks by learning corresponding parameter sharing patterns from data. Our method can provably represent equivariance-inducing parameter sharing for any finite group of symmetry transformations. Our experiments suggest that it can automatically learn to encode equivariances to common transformations used in image processing tasks. | https://openreview.net/pdf/eeb98a32337d0e4cdf8bb6b8073933c781bbf51e.pdf |
A Geometric Analysis of Deep Generative Image Models and Its Applications | https://openreview.net/forum?id=GH7QRzUDdXG | https://openreview.net/forum?id=GH7QRzUDdXG | Binxu Wang,Carlos R Ponce | ICLR 2021,Poster | Generative adversarial networks (GANs) have emerged as a powerful unsupervised method to model the statistical patterns of real-world data sets, such as natural images. These networks are trained to map random inputs in their latent space to new samples representative of the learned data. However, the structure of the latent space is hard to intuit due to its high dimensionality and the non-linearity of the generator, which limits the usefulness of the models. Understanding the latent space requires a way to identify input codes for existing real-world images (inversion), and a way to identify directions with known image transformations (interpretability). Here, we use a geometric framework to address both issues simultaneously. We develop an architecture-agnostic method to compute the Riemannian metric of the image manifold created by GANs. The eigen-decomposition of the metric isolates axes that account for different levels of image variability. An empirical analysis of several pretrained GANs shows that image variation around each position is concentrated along surprisingly few major axes (the space is highly anisotropic) and the directions that create this large variation are similar at different positions in the space (the space is homogeneous). We show that many of the top eigenvectors correspond to interpretable transforms in the image space, with a substantial part of eigenspace corresponding to minor transforms which could be compressed out. This geometric understanding unifies key previous results related to GAN interpretability. We show that the use of this metric allows for more efficient optimization in the latent space (e.g. GAN inversion) and facilitates unsupervised discovery of interpretable axes. Our results illustrate that defining the geometry of the GAN image manifold can serve as a general framework for understanding GANs. | https://openreview.net/pdf/6c6825f68048a90722aee1790a7d0f3d24ad2d55.pdf |
What Makes Instance Discrimination Good for Transfer Learning? | https://openreview.net/forum?id=tC6iW2UUbJf | https://openreview.net/forum?id=tC6iW2UUbJf | Nanxuan Zhao,Zhirong Wu,Rynson W. H. Lau,Stephen Lin | ICLR 2021,Poster | Contrastive visual pretraining based on the instance discrimination pretext task has made significant progress. Notably, recent work on unsupervised pretraining has shown to surpass the supervised counterpart for finetuning downstream applications such as object detection and segmentation. It comes as a surprise that image annotations would be better left unused for transfer learning. In this work, we investigate the following problems: What makes instance discrimination pretraining good for transfer learning? What knowledge is actually learned and transferred from these models? From this understanding of instance discrimination, how can we better exploit human annotation labels for pretraining? Our findings are threefold. First, what truly matters for the transfer is low-level and mid-level representations, not high-level representations. Second, the intra-category invariance enforced by the traditional supervised model weakens transferability by increasing task misalignment. Finally, supervised pretraining can be strengthened by following an exemplar-based approach without explicit constraints among the instances within the same category. | https://openreview.net/pdf/a051d411b95ea8650fc38090dc6150f3d948d35c.pdf |
Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval | https://openreview.net/forum?id=EMHoBG0avc1 | https://openreview.net/forum?id=EMHoBG0avc1 | Wenhan Xiong,Xiang Li,Srini Iyer,Jingfei Du,Patrick Lewis,William Yang Wang,Yashar Mehdad,Scott Yih,Sebastian Riedel,Douwe Kiela,Barlas Oguz | ICLR 2021,Poster | We propose a simple and efficient multi-hop dense retrieval approach for answering complex open-domain questions, which achieves state-of-the-art performance on two multi-hop datasets, HotpotQA and multi-evidence FEVER. Contrary to previous work, our method does not require access to any corpus-specific information, such as inter-document hyperlinks or human-annotated entity markers, and can be applied to any unstructured text corpus. Our system also yields a much better efficiency-accuracy trade-off, matching the best published accuracy on HotpotQA while being 10 times faster at inference time. | https://openreview.net/pdf/8c3ef7eb830c4416ddd0a429da7c1428e1e4d7bf.pdf |
On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections | https://openreview.net/forum?id=xgGS6PmzNq6 | https://openreview.net/forum?id=xgGS6PmzNq6 | Peizhao Li,Yifei Wang,Han Zhao,Pengyu Hong,Hongfu Liu | ICLR 2021,Poster | Disparate impact has raised serious concerns in machine learning applications and its societal impacts. In response to the need of mitigating discrimination, fairness has been regarded as a crucial property in algorithmic design. In this work, we study the problem of disparate impact on graph-structured data. Specifically, we focus on dyadic fairness, which articulates a fairness concept that a predictive relationship between two instances should be independent of the sensitive attributes. Based on this, we theoretically relate the graph connections to dyadic fairness on link predictive scores in learning graph neural networks, and reveal that regulating weights on existing edges in a graph contributes to dyadic fairness conditionally. Subsequently, we propose our algorithm, \textbf{FairAdj}, to empirically learn a fair adjacency matrix with proper graph structural constraints for fair link prediction, and in the meanwhile preserve predictive accuracy as much as possible. Empirical validation demonstrates that our method delivers effective dyadic fairness in terms of various statistics, and at the same time enjoys a favorable fairness-utility tradeoff. | https://openreview.net/pdf/752c587f7b01688e1a6372a843b7256cf17bad0a.pdf |
Spatio-Temporal Graph Scattering Transform | https://openreview.net/forum?id=CF-ZIuSMXRz | https://openreview.net/forum?id=CF-ZIuSMXRz | Chao Pan,Siheng Chen,Antonio Ortega | ICLR 2021,Poster | Although spatio-temporal graph neural networks have achieved great empirical success in handling multiple correlated time series, they may be impractical in some real-world scenarios due to a lack of sufficient high-quality training data. Furthermore, spatio-temporal graph neural networks lack theoretical interpretation. To address these issues, we put forth a novel mathematically designed framework to analyze spatio-temporal data. Our proposed spatio-temporal graph scattering transform (ST-GST) extends traditional scattering transform to the spatio-temporal domain. It performs iterative applications of spatio-temporal graph wavelets and nonlinear activation functions, which can be viewed as a forward pass of spatio-temporal graph convolutional networks without training. Since all the filter coefficients in ST-GST are mathematically designed, it is promising for the real-world scenarios with limited training data, and also allows for a theoretical analysis, which shows that the proposed ST-GST is stable to small perturbations of input signals and structures. Finally, our experiments show that i) ST-GST outperforms spatio-temporal graph convolutional networks by an increase of 35% in accuracy for MSR Action3D dataset; ii) it is better and computationally more efficient to design the transform based on separable spatio-temporal graphs than the joint ones; and iii) nonlinearity in ST-GST is critical to empirical performance. | https://openreview.net/pdf/7235bad64aa2f91b72c24d2ccadf7a03fb3e47e3.pdf |
MODALS: Modality-agnostic Automated Data Augmentation in the Latent Space | https://openreview.net/forum?id=XjYgR6gbCEc | https://openreview.net/forum?id=XjYgR6gbCEc | Tsz-Him Cheung,Dit-Yan Yeung | ICLR 2021,Poster | Data augmentation is an efficient way to expand a training dataset by creating additional artificial data. While data augmentation is found to be effective in improving the generalization capabilities of models for various machine learning tasks, the underlying augmentation methods are usually manually designed and carefully evaluated for each data modality separately, like image processing functions for image data and word-replacing rules for text data. In this work, we propose an automated data augmentation approach called MODALS (Modality-agnostic Automated Data Augmentation in the Latent Space) to augment data for any modality in a generic way. MODALS exploits automated data augmentation to fine-tune four universal data transformation operations in the latent space to adapt the transform to data of different modalities. Through comprehensive experiments, we demonstrate the effectiveness of MODALS on multiple datasets for text, tabular, time-series and image modalities. | https://openreview.net/pdf/83d8754b7f6d39240200978f99334ce755678fb1.pdf |
ALFWorld: Aligning Text and Embodied Environments for Interactive Learning | https://openreview.net/forum?id=0IOX0YcCdTn | https://openreview.net/forum?id=0IOX0YcCdTn | Mohit Shridhar,Xingdi Yuan,Marc-Alexandre Cote,Yonatan Bisk,Adam Trischler,Matthew Hausknecht | ICLR 2021,Poster | Given a simple request like Put a washed apple in the kitchen fridge, humans can reason in purely abstract terms by imagining action sequences and scoring their likelihood of success, prototypicality, and efficiency, all without moving a muscle. Once we see the kitchen in question, we can update our abstract plans to fit the scene. Embodied agents require the same abilities, but existing work does not yet provide the infrastructure necessary for both reasoning abstractly and executing concretely. We address this limitation by introducing ALFWorld, a simulator that enables agents to learn abstract, text-based policies in TextWorld (Côté et al., 2018) and then execute goals from the ALFRED benchmark (Shridhar et al., 2020) in a rich visual environment. ALFWorld enables the creation of a new BUTLER agent whose abstract knowledge, learned in TextWorld, corresponds directly to concrete, visually grounded actions. In turn, as we demonstrate empirically, this fosters better agent generalization than training only in the visually grounded environment. BUTLER’s simple, modular design factors the problem to allow researchers to focus on models for improving every piece of the pipeline (language understanding, planning, navigation, and visual scene understanding). | https://openreview.net/pdf/2699e7c1c941440d50a4e58fd4e05a89f9e10eb1.pdf |
Latent Skill Planning for Exploration and Transfer | https://openreview.net/forum?id=jXe91kq3jAq | https://openreview.net/forum?id=jXe91kq3jAq | Kevin Xie,Homanga Bharadhwaj,Danijar Hafner,Animesh Garg,Florian Shkurti | ICLR 2021,Poster | To quickly solve new tasks in complex environments, intelligent agents need to build up reusable knowledge. For example, a learned world model captures knowledge about the environment that applies to new tasks. Similarly, skills capture general behaviors that can apply to new tasks. In this paper, we investigate how these two approaches can be integrated into a single reinforcement learning agent. Specifically, we leverage the idea of partial amortization for fast adaptation at test time. For this, actions are produced by a policy that is learned over time while the skills it conditions on are chosen using online planning. We demonstrate the benefits of our design decisions across a suite of challenging locomotion tasks and demonstrate improved sample efficiency in single tasks as well as in transfer from one task to another, as compared to competitive baselines. Videos are available at: https://sites.google.com/view/latent-skill-planning/ | https://openreview.net/pdf/fe4b11b44759390e66cce050dfae61286ab48d51.pdf |
Evaluating the Disentanglement of Deep Generative Models through Manifold Topology | https://openreview.net/forum?id=djwS0m4Ft_A | https://openreview.net/forum?id=djwS0m4Ft_A | Sharon Zhou,Eric Zelikman,Fred Lu,Andrew Y. Ng,Gunnar E. Carlsson,Stefano Ermon | ICLR 2021,Poster | Learning disentangled representations is regarded as a fundamental task for improving the generalization, robustness, and interpretability of generative models. However, measuring disentanglement has been challenging and inconsistent, often dependent on an ad-hoc external model or specific to a certain dataset. To address this, we present a method for quantifying disentanglement that only uses the generative model, by measuring the topological similarity of conditional submanifolds in the learned representation. This method showcases both unsupervised and supervised variants. To illustrate the effectiveness and applicability of our method, we empirically evaluate several state-of-the-art models across multiple datasets. We find that our method ranks models similarly to existing methods. We make our code publicly available at https://github.com/stanfordmlgroup/disentanglement. | https://openreview.net/pdf/1a87763ff698ed701fa9648dad3eeb0fee6bb67b.pdf |
Combining Ensembles and Data Augmentation Can Harm Your Calibration | https://openreview.net/forum?id=g11CZSghXyY | https://openreview.net/forum?id=g11CZSghXyY | Yeming Wen,Ghassen Jerfel,Rafael Muller,Michael W Dusenberry,Jasper Snoek,Balaji Lakshminarayanan,Dustin Tran | ICLR 2021,Poster | Ensemble methods which average over multiple neural network predictions are a simple approach to improve a model’s calibration and robustness. Similarly, data augmentation techniques, which encode prior information in the form of invariant feature transformations, are effective for improving calibration and robustness. In this paper, we show a surprising pathology: combining ensembles and data augmentation can harm model calibration. This leads to a trade-off in practice, whereby improved accuracy by combining the two techniques comes at the expense of calibration. On the other hand, selecting only one of the techniques ensures good uncertainty estimates at the expense of accuracy. We investigate this pathology and identify a compounding under-confidence among methods which marginalize over sets of weights and data augmentation techniques which soften labels. Finally, we propose a simple correction, achieving the best of both worlds with significant accuracy and calibration gains over using only ensembles or data augmentation individually. Applying the correction produces new state-of-the art in uncertainty calibration and robustness across CIFAR-10, CIFAR-100, and ImageNet. | https://openreview.net/pdf/875497073e0d6dda023f1158c03b2fbccf68aaea.pdf |
CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers | https://openreview.net/forum?id=eom0IUrF__F | https://openreview.net/forum?id=eom0IUrF__F | SHIYANG LI,Semih Yavuz,Kazuma Hashimoto,Jia Li,Tong Niu,Nazneen Rajani,Xifeng Yan,Yingbo Zhou,Caiming Xiong | ICLR 2021,Poster | Dialogue state trackers have made significant progress on benchmark datasets, but their generalization capability to novel and realistic scenarios beyond the held- out conversations is less understood. We propose controllable counterfactuals (COCO) to bridge this gap and evaluate dialogue state tracking (DST) models on novel scenarios, i.e., would the system successfully tackle the request if the user responded differently but still consistently with the dialogue flow? COCO leverages turn-level belief states as counterfactual conditionals to produce novel conversation scenarios in two steps: (i) counterfactual goal generation at turn- level by dropping and adding slots followed by replacing slot values, (ii) counterfactual conversation generation that is conditioned on (i) and consistent with the dialogue flow. Evaluating state-of-the-art DST models on MultiWOZ dataset with COCO-generated counterfactuals results in a significant performance drop of up to 30.8% (from 49.4% to 18.6%) in absolute joint goal accuracy. In comparison, widely used techniques like paraphrasing only affect the accuracy by at most 2%. Human evaluations show that COCO-generated conversations perfectly reflect the underlying user goal with more than 95% accuracy and are as human-like as the original conversations, further strengthening its reliability and promise to be adopted as part of the robustness evaluation of DST models. | https://openreview.net/pdf/d1ab54448d631f7bb1bc618c89dbf557eb97e930.pdf |
AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models | https://openreview.net/forum?id=QkRbdiiEjM | https://openreview.net/forum?id=QkRbdiiEjM | Ke Sun,Zhanxing Zhu,Zhouchen Lin | ICLR 2021,Poster | The design of deep graph models still remains to be investigated and the crucial part is how to explore and exploit the knowledge from different hops of neighbors in an efficient way. In this paper, we propose a novel RNN-like deep graph neural network architecture by incorporating AdaBoost into the computation of network; and the proposed graph convolutional network called AdaGCN~(Adaboosting Graph Convolutional Network) has the ability to efficiently extract knowledge from high-order neighbors of current nodes and then integrates knowledge from different hops of neighbors into the network in an Adaboost way. Different from other graph neural networks that directly stack many graph convolution layers, AdaGCN shares the same base neural network architecture among all ``layers'' and is recursively optimized, which is similar to an RNN. Besides, We also theoretically established the connection between AdaGCN and existing graph convolutional methods, presenting the benefits of our proposal. Finally, extensive experiments demonstrate the consistent state-of-the-art prediction performance on graphs across different label rates and the computational advantage of our approach AdaGCN~\footnote{Code is available at \url{https://github.com/datake/AdaGCN}.}. | https://openreview.net/pdf/f3d1211b83f6d62eeef81117c15818464a995abb.pdf |
Learning What To Do by Simulating the Past | https://openreview.net/forum?id=kBVJ2NtiY- | https://openreview.net/forum?id=kBVJ2NtiY- | David Lindner,Rohin Shah,Pieter Abbeel,Anca Dragan | ICLR 2021,Poster | Since reward functions are hard to specify, recent work has focused on learning policies from human feedback. However, such approaches are impeded by the expense of acquiring such feedback. Recent work proposed that agents have access to a source of information that is effectively free: in any environment that humans have acted in, the state will already be optimized for human preferences, and thus an agent can extract information about what humans want from the state. Such learning is possible in principle, but requires simulating all possible past trajectories that could have led to the observed state. This is feasible in gridworlds, but how do we scale it to complex tasks? In this work, we show that by combining a learned feature encoder with learned inverse models, we can enable agents to simulate human actions backwards in time to infer what they must have done. The resulting algorithm is able to reproduce a specific skill in MuJoCo environments given a single state sampled from the optimal policy for that skill. | https://openreview.net/pdf/42dbf5584d4a07037b331115be1963b8650f85fd.pdf |
CcGAN: Continuous Conditional Generative Adversarial Networks for Image Generation | https://openreview.net/forum?id=PrzjugOsDeE | https://openreview.net/forum?id=PrzjugOsDeE | Xin Ding,Yongwei Wang,Zuheng Xu,William J Welch,Z. Jane Wang | ICLR 2021,Poster | This work proposes the continuous conditional generative adversarial network (CcGAN), the first generative model for image generation conditional on continuous, scalar conditions (termed regression labels). Existing conditional GANs (cGANs) are mainly designed for categorical conditions (e.g., class labels); conditioning on a continuous label is mathematically distinct and raises two fundamental problems: (P1) Since there may be very few (even zero) real images for some regression labels, minimizing existing empirical versions of cGAN losses (a.k.a. empirical cGAN losses) often fails in practice; (P2) Since regression labels are scalar and infinitely many, conventional label input methods (e.g., combining a hidden map of the generator/discriminator with a one-hot encoded label) are not applicable. The proposed CcGAN solves the above problems, respectively, by (S1) reformulating existing empirical cGAN losses to be appropriate for the continuous scenario; and (S2) proposing a novel method to incorporate regression labels into the generator and the discriminator. The reformulation in (S1) leads to two novel empirical discriminator losses, termed the hard vicinal discriminator loss (HVDL) and the soft vicinal discriminator loss (SVDL) respectively, and a novel empirical generator loss. The error bounds of a discriminator trained with HVDL and SVDL are derived under mild assumptions in this work. A new benchmark dataset, RC-49, is also proposed for generative image modeling conditional on regression labels. Our experiments on the Circular 2-D Gaussians, RC-49, and UTKFace datasets show that CcGAN is able to generate diverse, high-quality samples from the image distribution conditional on a given regression label. Moreover, in these experiments, CcGAN substantially outperforms cGAN both visually and quantitatively. | https://openreview.net/pdf/e0bbc6b8950c086438fe77cc0c9098bff1128577.pdf |
ANOCE: Analysis of Causal Effects with Multiple Mediators via Constrained Structural Learning | https://openreview.net/forum?id=7I12hXRi8F | https://openreview.net/forum?id=7I12hXRi8F | Hengrui Cai,Rui Song,Wenbin Lu | ICLR 2021,Poster | In the era of causal revolution, identifying the causal effect of an exposure on the outcome of interest is an important problem in many areas, such as epidemics, medicine, genetics, and economics. Under a general causal graph, the exposure may have a direct effect on the outcome and also an indirect effect regulated by a set of mediators. An analysis of causal effects that interprets the causal mechanism contributed through mediators is hence challenging but on demand. To the best of our knowledge, there are no feasible algorithms that give an exact decomposition of the indirect effect on the level of individual mediators, due to common interaction among mediators in the complex graph. In this paper, we establish a new statistical framework to comprehensively characterize causal effects with multiple mediators, namely, ANalysis Of Causal Effects (ANOCE), with a newly introduced definition of the mediator effect, under the linear structure equation model. We further propose a constrained causal structure learning method by incorporating a novel identification constraint that specifies the temporal causal relationship of variables. The proposed algorithm is applied to investigate the causal effects of 2020 Hubei lockdowns on reducing the spread of the coronavirus in Chinese major cities out of Hubei. | https://openreview.net/pdf/8142413a92e7df5fb79598dee863640346d53f5b.pdf |
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate | https://openreview.net/forum?id=3X64RLgzY6O | https://openreview.net/forum?id=3X64RLgzY6O | Jingfeng Wu,Difan Zou,Vladimir Braverman,Quanquan Gu | ICLR 2021,Poster | Understanding the algorithmic bias of stochastic gradient descent (SGD) is one of the key challenges in modern machine learning and deep learning theory. Most of the existing works, however, focus on very small or even infinitesimal learning rate regime, and fail to cover practical scenarios where the learning rate is moderate and annealing. In this paper, we make an initial attempt to characterize the particular regularization effect of SGD in the moderate learning rate regime by studying its behavior for optimizing an overparameterized linear regression problem. In this case, SGD and GD are known to converge to the unique minimum-norm solution; however, with the moderate and annealing learning rate, we show that they exhibit different directional bias: SGD converges along the large eigenvalue directions of the data matrix, while GD goes after the small eigenvalue directions. Furthermore, we show that such directional bias does matter when early stopping is adopted, where the SGD output is nearly optimal but the GD output is suboptimal. Finally, our theory explains several folk arts in practice used for SGD hyperparameter tuning, such as (1) linearly scaling the initial learning rate with batch size; and (2) overrunning SGD with high learning rate even when the loss stops decreasing. | https://openreview.net/pdf/48577f49989616efeba863fc8477794a13b5d90e.pdf |
Robust early-learning: Hindering the memorization of noisy labels | https://openreview.net/forum?id=Eql5b1_hTE4 | https://openreview.net/forum?id=Eql5b1_hTE4 | Xiaobo Xia,Tongliang Liu,Bo Han,Chen Gong,Nannan Wang,Zongyuan Ge,Yi Chang | ICLR 2021,Poster | The \textit{memorization effects} of deep networks show that they will first memorize training data with clean labels and then those with noisy labels. The \textit{early stopping} method therefore can be exploited for learning with noisy labels. However, the side effect brought by noisy labels will influence the memorization of clean labels before early stopping. In this paper, motivated by the \textit{lottery ticket hypothesis} which shows that only partial parameters are important for generalization, we find that only partial parameters are important for fitting clean labels and generalize well, which we term as \textit{critical parameters}; while the other parameters tend to fit noisy labels and cannot generalize well, which we term as \textit{non-critical parameters}. Based on this, we propose \textit{robust early-learning} to reduce the side effect of noisy labels before early stopping and thus enhance the memorization of clean labels. Specifically, in each iteration, we divide all parameters into the critical and non-critical ones, and then perform different update rules for different types of parameters. Extensive experiments on benchmark-simulated and real-world label-noise datasets demonstrate the superiority of the proposed method over the state-of-the-art label-noise learning methods. | https://openreview.net/pdf/8bcbd9a8ffef76580768ad0f329dcc1cae3be97e.pdf |
A Design Space Study for LISTA and Beyond | https://openreview.net/forum?id=GMgHyUPrXa | https://openreview.net/forum?id=GMgHyUPrXa | Tianjian Meng,Xiaohan Chen,Yifan Jiang,Zhangyang Wang | ICLR 2021,Poster | In recent years, great success has been witnessed in building problem-specific deep networks from unrolling iterative algorithms, for solving inverse problems and beyond. Unrolling is believed to incorporate the model-based prior with the learning capacity of deep learning. This paper revisits \textit{the role of unrolling as a design approach for deep networks}: to what extent its resulting special architecture is superior, and can we find better? Using LISTA for sparse recovery as a representative example, we conduct the first thorough \textit{design space study} for the unrolled models. Among all possible variations, we focus on extensively varying the connectivity patterns and neuron types, leading to a gigantic design space arising from LISTA. To efficiently explore this space and identify top performers, we leverage the emerging tool of neural architecture search (NAS). We carefully examine the searched top architectures in a number of settings, and are able to discover networks that consistently better than LISTA. We further present more visualization and analysis to ``open the black box", and find that the searched top architectures demonstrate highly consistent and potentially transferable patterns. We hope our study to spark more reflections and explorations on how to better mingle model-based optimization prior and data-driven learning. | https://openreview.net/pdf/f6321fc76124be68a636149a64dea811815c73a0.pdf |
PAC Confidence Predictions for Deep Neural Network Classifiers | https://openreview.net/forum?id=Qk-Wq5AIjpq | https://openreview.net/forum?id=Qk-Wq5AIjpq | Sangdon Park,Shuo Li,Insup Lee,Osbert Bastani | ICLR 2021,Poster | A key challenge for deploying deep neural networks (DNNs) in safety critical settings is the need to provide rigorous ways to quantify their uncertainty. In this paper, we propose a novel algorithm for constructing predicted classification confidences for DNNs that comes with provable correctness guarantees. Our approach uses Clopper-Pearson confidence intervals for the Binomial distribution in conjunction with the histogram binning approach to calibrated prediction. In addition, we demonstrate how our predicted confidences can be used to enable downstream guarantees in two settings: (i) fast DNN inference, where we demonstrate how to compose a fast but inaccurate DNN with an accurate but slow DNN in a rigorous way to improve performance without sacrificing accuracy, and (ii) safe planning, where we guarantee safety when using a DNN to predict whether a given action is safe based on visual observations. In our experiments, we demonstrate that our approach can be used to provide guarantees for state-of-the-art DNNs. | https://openreview.net/pdf/722efb9d50df44cb8e7fca2dbab280da02707515.pdf |
X2T: Training an X-to-Text Typing Interface with Online Learning from User Feedback | https://openreview.net/forum?id=LiX3ECzDPHZ | https://openreview.net/forum?id=LiX3ECzDPHZ | Jensen Gao,Siddharth Reddy,Glen Berseth,Nicholas Hardy,Nikhilesh Natraj,Karunesh Ganguly,Anca Dragan,Sergey Levine | ICLR 2021,Poster | We aim to help users communicate their intent to machines using flexible, adaptive interfaces that translate arbitrary user input into desired actions. In this work, we focus on assistive typing applications in which a user cannot operate a keyboard, but can instead supply other inputs, such as webcam images that capture eye gaze or neural activity measured by a brain implant. Standard methods train a model on a fixed dataset of user inputs, then deploy a static interface that does not learn from its mistakes; in part, because extracting an error signal from user behavior can be challenging. We investigate a simple idea that would enable such interfaces to improve over time, with minimal additional effort from the user: online learning from user feedback on the accuracy of the interface's actions. In the typing domain, we leverage backspaces as feedback that the interface did not perform the desired action. We propose an algorithm called x-to-text (X2T) that trains a predictive model of this feedback signal, and uses this model to fine-tune any existing, default interface for translating user input into actions that select words or characters. We evaluate X2T through a small-scale online user study with 12 participants who type sentences by gazing at their desired words, a large-scale observational study on handwriting samples from 60 users, and a pilot study with one participant using an electrocorticography-based brain-computer interface. The results show that X2T learns to outperform a non-adaptive default interface, stimulates user co-adaptation to the interface, personalizes the interface to individual users, and can leverage offline data collected from the default interface to improve its initial performance and accelerate online learning. | https://openreview.net/pdf/b299e92f993e7b50461323c326ad974dc5b67e09.pdf |
Discrete Graph Structure Learning for Forecasting Multiple Time Series | https://openreview.net/forum?id=WEHSlH5mOk | https://openreview.net/forum?id=WEHSlH5mOk | Chao Shang,Jie Chen,Jinbo Bi | ICLR 2021,Poster | Time series forecasting is an extensively studied subject in statistics, economics, and computer science. Exploration of the correlation and causation among the variables in a multivariate time series shows promise in enhancing the performance of a time series model. When using deep neural networks as forecasting models, we hypothesize that exploiting the pairwise information among multiple (multivariate) time series also improves their forecast. If an explicit graph structure is known, graph neural networks (GNNs) have been demonstrated as powerful tools to exploit the structure. In this work, we propose learning the structure simultaneously with the GNN if the graph is unknown. We cast the problem as learning a probabilistic graph model through optimizing the mean performance over the graph distribution. The distribution is parameterized by a neural network so that discrete graphs can be sampled differentiably through reparameterization. Empirical evaluations show that our method is simpler, more efficient, and better performing than a recently proposed bilevel learning approach for graph structure learning, as well as a broad array of forecasting models, either deep or non-deep learning based, and graph or non-graph based. | https://openreview.net/pdf/9bb821e246b6c153951a4bad60d4e0881a77e909.pdf |
Task-Agnostic Morphology Evolution | https://openreview.net/forum?id=CGQ6ENUMX6 | https://openreview.net/forum?id=CGQ6ENUMX6 | Donald Joseph Hejna III,Pieter Abbeel,Lerrel Pinto | ICLR 2021,Poster | Deep reinforcement learning primarily focuses on learning behavior, usually overlooking the fact that an agent's function is largely determined by form. So, how should one go about finding a morphology fit for solving tasks in a given environment? Current approaches that co-adapt morphology and behavior use a specific task's reward as a signal for morphology optimization. However, this often requires expensive policy optimization and results in task-dependent morphologies that are not built to generalize. In this work, we propose a new approach, Task-Agnostic Morphology Evolution (TAME), to alleviate both of these issues. Without any task or reward specification, TAME evolves morphologies by only applying randomly sampled action primitives on a population of agents. This is accomplished using an information-theoretic objective that efficiently ranks agents by their ability to reach diverse states in the environment and the causality of their actions. Finally, we empirically demonstrate that across 2D, 3D, and manipulation environments TAME can evolve morphologies that match the multi-task performance of those learned with task supervised algorithms. Our code and videos can be found at https://sites.google.com/view/task-agnostic-evolution .
| https://openreview.net/pdf/8548ea3c5369fd4bf3a020ff1c71f973bf77a3b3.pdf |
Deep Equals Shallow for ReLU Networks in Kernel Regimes | https://openreview.net/forum?id=aDjoksTpXOP | https://openreview.net/forum?id=aDjoksTpXOP | Alberto Bietti,Francis Bach | ICLR 2021,Poster | Deep networks are often considered to be more expressive than shallow ones in terms of approximation. Indeed, certain functions can be approximated by deep networks provably more efficiently than by shallow ones, however, no tractable algorithms are known for learning such deep models. Separately, a recent line of work has shown that deep networks trained with gradient descent may behave like (tractable) kernel methods in a certain over-parameterized regime, where the kernel is determined by the architecture and initialization, and this paper focuses on approximation for such kernels. We show that for ReLU activations, the kernels derived from deep fully-connected networks have essentially the same approximation properties as their shallow two-layer counterpart, namely the same eigenvalue decay for the corresponding integral operator. This highlights the limitations of the kernel framework for understanding the benefits of such deep architectures. Our main theoretical result relies on characterizing such eigenvalue decays through differentiability properties of the kernel function, which also easily applies to the study of other kernels defined on the sphere. | https://openreview.net/pdf/05eb0aa9a9e483722161a491e50829bba7751f6b.pdf |
Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search | https://openreview.net/forum?id=5jzlpHvvRk | https://openreview.net/forum?id=5jzlpHvvRk | Peidong Liu,Gengwei Zhang,Bochao Wang,Hang Xu,Xiaodan Liang,Yong Jiang,Zhenguo Li | ICLR 2021,Poster | Designing proper loss functions for vision tasks has been a long-standing research direction to advance the capability of existing models. For object detection, the well-established classification and regression loss functions have been carefully designed by considering diverse learning challenges (e.g. class imbalance, hard negative samples, and scale variances). Inspired by the recent progress in network architecture search, it is interesting to explore the possibility of discovering new loss function formulations via directly searching the primitive operation combinations. So that the learned losses not only fit for diverse object detection challenges to alleviate huge human efforts, but also have better alignment with evaluation metric and good mathematical convergence property. Beyond the previous auto-loss works on face recognition and image classification, our work makes the first attempt to discover new loss functions for the challenging object detection from primitive operation levels and finds the searched losses are insightful. We propose an effective convergence-simulation driven evolutionary search algorithm, called CSE-Autoloss, for speeding up the search progress by regularizing the mathematical rationality of loss candidates via two progressive convergence simulation modules: convergence property verification and model optimization simulation. CSE-Autoloss involves the search space (i.e. 21 mathematical operators, 3 constant-type inputs, and 3 variable-type inputs) that cover a wide range of the possible variants of existing losses and discovers best-searched loss function combination within a short time (around 1.5 wall-clock days with 20x speedup in comparison to the vanilla evolutionary algorithm). We conduct extensive evaluations of loss function search on popular detectors and validate the good generalization capability of searched losses across diverse architectures and various datasets. Our experiments show that the best-discovered loss function combinations outperform default combinations (Cross-entropy/Focal loss for classification and L1 loss for regression) by 1.1% and 0.8% in terms of mAP for two-stage and one-stage detectors on COCO respectively. Our searched losses are available at https://github.com/PerdonLiu/CSE-Autoloss. | https://openreview.net/pdf/7980b823a80f5de80077d19818798a08c96ab635.pdf |
Effective Distributed Learning with Random Features: Improved Bounds and Algorithms | https://openreview.net/forum?id=jxdXSW9Doc | https://openreview.net/forum?id=jxdXSW9Doc | Yong Liu,Jiankun Liu,Shuqiang Wang | ICLR 2021,Poster | In this paper, we study the statistical properties of distributed kernel ridge regression together with random features (DKRR-RF), and obtain optimal generalization bounds under the basic setting, which can substantially relax the restriction on the number of local machines in the existing state-of-art bounds. Specifically, we first show that the simple combination of divide-and-conquer technique and random features can achieve the same statistical accuracy as the exact KRR in expectation requiring only $\mathcal{O}(|\mathcal{D}|)$ memory and $\mathcal{O}(|\mathcal{D}|^{1.5})$ time. Then, beyond the generalization bounds in expectation that demonstrate the average information for multiple trails, we derive generalization bounds in probability to capture the learning performance for a single trail. Finally, we propose an effective communication strategy to further improve the performance of DKRR-RF, and validate the theoretical bounds via numerical experiments. | https://openreview.net/pdf/79a6a49d47bc7c0250c2c92272851fefc0880cb6.pdf |
On InstaHide, Phase Retrieval, and Sparse Matrix Factorization | https://openreview.net/forum?id=AhElGnhU2BV | https://openreview.net/forum?id=AhElGnhU2BV | Sitan Chen,Xiaoxiao Li,Zhao Song,Danyang Zhuo | ICLR 2021,Poster | In this work, we examine the security of InstaHide, a scheme recently proposed by \cite{hsla20} for preserving the security of private datasets in the context of distributed learning. To generate a synthetic training example to be shared among the distributed learners, InstaHide takes a convex combination of private feature vectors and randomly flips the sign of each entry of the resulting vector with probability 1/2. A salient question is whether this scheme is secure in any provable sense, perhaps under a plausible complexity-theoretic assumption.
The answer to this turns out to be quite subtle and closely related to the average-case complexity of a multi-task, missing-data version of the classic problem of phase retrieval that is interesting in its own right. Motivated by this connection, under the standard distributional assumption that the public/private feature vectors are isotropic Gaussian, we design an algorithm that can actually recover a private vector using only the public vectors and a sequence of synthetic vectors generated by InstaHide. | https://openreview.net/pdf/14d55d0812b8f1205f82c19f49a21c021d7b296e.pdf |
Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies | https://openreview.net/forum?id=Vd7lCMvtLqg | https://openreview.net/forum?id=Vd7lCMvtLqg | Paul Pu Liang,Manzil Zaheer,Yuan Wang,Amr Ahmed | ICLR 2021,Poster | Learning continuous representations of discrete objects such as text, users, movies, and URLs lies at the heart of many applications including language and user modeling. When using discrete objects as input to neural networks, we often ignore the underlying structures (e.g., natural groupings and similarities) and embed the objects independently into individual vectors. As a result, existing methods do not scale to large vocabulary sizes. In this paper, we design a simple and efficient embedding algorithm that learns a small set of anchor embeddings and a sparse transformation matrix. We call our method Anchor & Transform (ANT) as the embeddings of discrete objects are a sparse linear combination of the anchors, weighted according to the transformation matrix. ANT is scalable, flexible, and end-to-end trainable. We further provide a statistical interpretation of our algorithm as a Bayesian nonparametric prior for embeddings that encourages sparsity and leverages natural groupings among objects. By deriving an approximate inference algorithm based on Small Variance Asymptotics, we obtain a natural extension that automatically learns the optimal number of anchors instead of having to tune it as a hyperparameter. On text classification, language modeling, and movie recommendation benchmarks, we show that ANT is particularly suitable for large vocabulary sizes and demonstrates stronger performance with fewer parameters (up to 40x compression) as compared to existing compression baselines. | https://openreview.net/pdf/22fba9165ecde0ee1ac4f19e7d5c5d99fd4c5146.pdf |
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning | https://openreview.net/forum?id=jDdzh5ul-d | https://openreview.net/forum?id=jDdzh5ul-d | Haibo Yang,Minghong Fang,Jia Liu | ICLR 2021,Poster | Federated learning (FL) is a distributed machine learning architecture that leverages a large number of workers to jointly learn a model with decentralized data. FL has received increasing attention in recent years thanks to its data privacy protection, communication efficiency and a linear speedup for convergence in training (i.e., convergence performance increases linearly with respect to the number of workers). However, existing studies on linear speedup for convergence are only limited to the assumptions of i.i.d. datasets across workers and/or full worker participation, both of which rarely hold in practice. So far, it remains an open question whether or not the linear speedup for convergence is achievable under non-i.i.d. datasets with partial worker participation in FL. In this paper, we show that the answer is affirmative. Specifically, we show that the federated averaging (FedAvg) algorithm (with two-sided learning rates) on non-i.i.d. datasets in non-convex settings achieves a convergence rate $\mathcal{O}(\frac{1}{\sqrt{mKT}} + \frac{1}{T})$ for full worker participation and a convergence rate $\mathcal{O}(\frac{\sqrt{K}}{\sqrt{nT}} + \frac{1}{T})$ for partial worker participation, where $K$ is the number of local steps, $T$ is the number of total communication rounds, $m$ is the total worker number and $n$ is the worker number in one communication round if for partial worker participation. Our results also reveal that the local steps in FL could help the convergence and show that the maximum number of local steps can be improved to $T/m$ in full worker participation. We conduct extensive experiments on MNIST and CIFAR-10 to verify our theoretical results. | https://openreview.net/pdf/d80668746783bad5c0b3ff7eff505b3c40b825cd.pdf |
Deep Neural Tangent Kernel and Laplace Kernel Have the Same RKHS | https://openreview.net/forum?id=vK9WrZ0QYQ | https://openreview.net/forum?id=vK9WrZ0QYQ | Lin Chen,Sheng Xu | ICLR 2021,Poster | We prove that the reproducing kernel Hilbert spaces (RKHS) of a deep neural tangent kernel and the Laplace kernel include the same set of functions, when both kernels are restricted to the sphere $\mathbb{S}^{d-1}$. Additionally, we prove that the exponential power kernel with a smaller power (making the kernel less smooth) leads to a larger RKHS, when it is restricted to the sphere $\mathbb{S}^{d-1}$ and when it is defined on the entire $\mathbb{R}^d$. | https://openreview.net/pdf/7668a07e4bcb66d9e15b6e11078b8b4397d4f7d1.pdf |
Fast convergence of stochastic subgradient method under interpolation | https://openreview.net/forum?id=w2mYg3d0eot | https://openreview.net/forum?id=w2mYg3d0eot | Huang Fang,Zhenan Fan,Michael Friedlander | ICLR 2021,Poster | This paper studies the behaviour of the stochastic subgradient descent (SSGD) method applied to over-parameterized nonsmooth optimization problems that satisfy an interpolation condition. By leveraging the composite structure of the empirical risk minimization problems, we prove that SSGD converges, respectively, with rates $O(1/\epsilon)$ and $O(\log(1/\epsilon))$ for convex and strongly-convex objectives when interpolation holds. These rates coincide with established rates for the stochastic gradient descent (SGD) method applied to smooth problems that also satisfy an interpolation condition. Our analysis provides a partial explanation for the empirical observation that sometimes SGD and SSGD behave similarly for training smooth and nonsmooth machine learning models. We also prove that the rate $O(1/\epsilon)$ is optimal for the subgradient method in the convex and interpolation setting. | https://openreview.net/pdf/a65651e46213dfe6307698d82f362d04acd34756.pdf |
Generating Adversarial Computer Programs using Optimized Obfuscations | https://openreview.net/forum?id=PH5PH9ZO_4 | https://openreview.net/forum?id=PH5PH9ZO_4 | Shashank Srikant,Sijia Liu,Tamara Mitrovska,Shiyu Chang,Quanfu Fan,Gaoyuan Zhang,Una-May O'Reilly | ICLR 2021,Poster | Machine learning (ML) models that learn and predict properties of computer programs are increasingly being adopted and deployed.
These models have demonstrated success in applications such as auto-completing code, summarizing large programs, and detecting bugs and malware in programs.
In this work, we investigate principled ways to adversarially perturb a computer program to fool such learned models, and thus determine their adversarial robustness. We use program obfuscations, which have conventionally been used to avoid attempts at reverse engineering programs, as adversarial perturbations. These perturbations modify programs in ways that do not alter their functionality but can be crafted to deceive an ML model when making a decision. We provide a general formulation for an adversarial program that allows applying multiple obfuscation transformations to a program in any language. We develop first-order optimization algorithms to efficiently determine two key aspects -- which parts of the program to transform, and what transformations to use. We show that it is important to optimize both these aspects to generate the best adversarially perturbed program. Due to the discrete nature of this problem, we also propose using randomized smoothing to improve the attack loss landscape to ease optimization.
We evaluate our work on Python and Java programs on the problem of program summarization.
We show that our best attack proposal achieves a $52\%$ improvement over a state-of-the-art attack generation approach for programs trained on a \textsc{seq2seq} model.
We further show that our formulation is better at training models that are robust to adversarial attacks. | https://openreview.net/pdf/a1ea35be61a08768851add1266a1d1ef645a5c34.pdf |
C-Learning: Learning to Achieve Goals via Recursive Classification | https://openreview.net/forum?id=tc5qisoB-C | https://openreview.net/forum?id=tc5qisoB-C | Benjamin Eysenbach,Ruslan Salakhutdinov,Sergey Levine | ICLR 2021,Poster | We study the problem of predicting and controlling the future state distribution of an autonomous agent. This problem, which can be viewed as a reframing of goal-conditioned reinforcement learning (RL), is centered around learning a conditional probability density function over future states. Instead of directly estimating this density function, we indirectly estimate this density function by training a classifier to predict whether an observation comes from the future. Via Bayes' rule, predictions from our classifier can be transformed into predictions over future states. Importantly, an off-policy variant of our algorithm allows us to predict the future state distribution of a new policy, without collecting new experience. This variant allows us to optimize functionals of a policy's future state distribution, such as the density of reaching a particular goal state. While conceptually similar to Q-learning, our work lays a principled foundation for goal-conditioned RL as density estimation, providing justification for goal-conditioned methods used in prior work. This foundation makes hypotheses about Q-learning, including the optimal goal-sampling ratio, which we confirm experimentally. Moreover, our proposed method is competitive with prior goal-conditioned RL methods. | https://openreview.net/pdf/6a06ad37cef81666dc0ffbc9cffba623fcb34843.pdf |
Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers | https://openreview.net/forum?id=eqBwg3AcIAK | https://openreview.net/forum?id=eqBwg3AcIAK | Benjamin Eysenbach,Shreyas Chaudhari,Swapnil Asawa,Sergey Levine,Ruslan Salakhutdinov | ICLR 2021,Poster | We propose a simple, practical, and intuitive approach for domain adaptation in reinforcement learning. Our approach stems from the idea that the agent's experience in the source domain should look similar to its experience in the target domain. Building off of a probabilistic view of RL, we achieve this goal by compensating for the difference in dynamics by modifying the reward function. This modified reward function is simple to estimate by learning auxiliary classifiers that distinguish source-domain transitions from target-domain transitions. Intuitively, the agent is penalized for transitions that would indicate that the agent is interacting with the source domain, rather than the target domain. Formally, we prove that applying our method in the source domain is guaranteed to obtain a near-optimal policy for the target domain, provided that the source and target domains satisfy a lightweight assumption. Our approach is applicable to domains with continuous states and actions and does not require learning an explicit model of the dynamics. On discrete and continuous control tasks, we illustrate the mechanics of our approach and demonstrate its scalability to high-dimensional~tasks. | https://openreview.net/pdf/cc68f287d3416da646309bf4689396ac35e2be5c.pdf |
Multi-resolution modeling of a discrete stochastic process identifies causes of cancer | https://openreview.net/forum?id=KtH8W3S_RE | https://openreview.net/forum?id=KtH8W3S_RE | Adam Uri Yaari,Maxwell Sherman,Oliver Clarke Priebe,Po-Ru Loh,Boris Katz,Andrei Barbu,Bonnie Berger | ICLR 2021,Poster | Detection of cancer-causing mutations within the vast and mostly unexplored human genome is a major challenge. Doing so requires modeling the background mutation rate, a highly non-stationary stochastic process, across regions of interest varying in size from one to millions of positions. Here, we present the split-Poisson-Gamma (SPG) distribution, an extension of the classical Poisson-Gamma formulation, to model a discrete stochastic process at multiple resolutions. We demonstrate that the probability model has a closed-form posterior, enabling efficient and accurate linear-time prediction over any length scale after the parameters of the model have been inferred a single time. We apply our framework to model mutation rates in tumors and show that model parameters can be accurately inferred from high-dimensional epigenetic data using a convolutional neural network, Gaussian process, and maximum-likelihood estimation. Our method is both more accurate and more efficient than existing models over a large range of length scales. We demonstrate the usefulness of multi-resolution modeling by detecting genomic elements that drive tumor emergence and are of vastly differing sizes. | https://openreview.net/pdf/ef004741f232acac82caf795ea408cbd814a413c.pdf |
On the Critical Role of Conventions in Adaptive Human-AI Collaboration | https://openreview.net/forum?id=8Ln-Bq0mZcy | https://openreview.net/forum?id=8Ln-Bq0mZcy | Andy Shih,Arjun Sawhney,Jovana Kondic,Stefano Ermon,Dorsa Sadigh | ICLR 2021,Poster | Humans can quickly adapt to new partners in collaborative tasks (e.g. playing basketball), because they understand which fundamental skills of the task (e.g. how to dribble, how to shoot) carry over across new partners. Humans can also quickly adapt to similar tasks with the same partners by carrying over conventions that they have developed (e.g. raising hand signals pass the ball), without learning to coordinate from scratch. To collaborate seamlessly with humans, AI agents should adapt quickly to new partners and new tasks as well. However, current approaches have not attempted to distinguish between the complexities intrinsic to a task and the conventions used by a partner, and more generally there has been little focus on leveraging conventions for adapting to new settings. In this work, we propose a learning framework that teases apart rule-dependent representation from convention-dependent representation in a principled way. We show that, under some assumptions, our rule-dependent representation is a sufficient statistic of the distribution over best-response strategies across partners. Using this separation of representations, our agents are able to adapt quickly to new partners, and to coordinate with old partners on new tasks in a zero-shot manner. We experimentally validate our approach on three collaborative tasks varying in complexity: a contextual multi-armed bandit, a block placing task, and the card game Hanabi. | https://openreview.net/pdf/ed63d04dd420c1736ec72390fdca146d1c0a4c85.pdf |
WaveGrad: Estimating Gradients for Waveform Generation | https://openreview.net/forum?id=NsMLjcFaO8O | https://openreview.net/forum?id=NsMLjcFaO8O | Nanxin Chen,Yu Zhang,Heiga Zen,Ron J Weiss,Mohammad Norouzi,William Chan | ICLR 2021,Poster | This paper introduces WaveGrad, a conditional model for waveform generation which estimates gradients of the data density. The model is built on prior work on score matching and diffusion probabilistic models. It starts from a Gaussian white noise signal and iteratively refines the signal via a gradient-based sampler conditioned on the mel-spectrogram.
WaveGrad offers a natural way to trade inference speed for sample quality by adjusting the number of refinement steps, and bridges the gap between non-autoregressive and autoregressive models in terms of audio quality.
We find that it can generate high fidelity audio samples using as few as six iterations.
Experiments reveal WaveGrad to generate high fidelity audio, outperforming adversarial non-autoregressive baselines and matching a strong likelihood-based autoregressive baseline using fewer sequential operations. Audio samples are available at https://wavegrad.github.io/. | https://openreview.net/pdf/511dfd60f5250df72c5b324c7fb22552789093e1.pdf |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.