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v139/raileanu21a | Decoupling Value and Policy for Generalization in Reinforcement Learning | https://proceedings.mlr.press/v139/raileanu21a.html | [
"Roberta Raileanu",
"Rob Fergus"
] | null | null | Standard deep reinforcement learning algorithms use a shared representation for the policy and value function, especially when training directly from images. However, we argue that more information is needed to accurately estimate the value function than to learn the optimal policy. Consequently, the use of a shared re... | [] | null | 801 | 2102.10330 | title_snapshot |
v139/rajagopalan21a | Hierarchical Clustering of Data Streams: Scalable Algorithms and Approximation Guarantees | https://proceedings.mlr.press/v139/rajagopalan21a.html | [
"Anand Rajagopalan",
"Fabio Vitale",
"Danny Vainstein",
"Gui Citovsky",
"Cecilia M Procopiuc",
"Claudio Gentile"
] | null | null | We investigate the problem of hierarchically clustering data streams containing metric data in R^d. We introduce a desirable invariance property for such algorithms, describe a general family of hyperplane-based methods enjoying this property, and analyze two scalable instances of this general family against recently p... | [] | null | 802 | null | null |
v139/rakotomamonjy21a | Differentially Private Sliced Wasserstein Distance | https://proceedings.mlr.press/v139/rakotomamonjy21a.html | [
"Alain Rakotomamonjy",
"Ralaivola Liva"
] | null | null | Developing machine learning methods that are privacy preserving is today a central topic of research, with huge practical impacts. Among the numerous ways to address privacy-preserving learning, we here take the perspective of computing the divergences between distributions under the Differential Privacy (DP) framework... | [] | null | 803 | 2107.01848 | title_snapshot |
v139/ramesh21a | Zero-Shot Text-to-Image Generation | https://proceedings.mlr.press/v139/ramesh21a.html | [
"Aditya Ramesh",
"Mikhail Pavlov",
"Gabriel Goh",
"Scott Gray",
"Chelsea Voss",
"Alec Radford",
"Mark Chen",
"Ilya Sutskever"
] | null | null | Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentation masks supplied during training. We describe a simple approach... | [] | null | 804 | 2102.12092 | title_snapshot |
v139/rangapuram21a | End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series | https://proceedings.mlr.press/v139/rangapuram21a.html | [
"Syama Sundar Rangapuram",
"Lucien D Werner",
"Konstantinos Benidis",
"Pedro Mercado",
"Jan Gasthaus",
"Tim Januschowski"
] | null | null | This paper presents a novel approach for hierarchical time series forecasting that produces coherent, probabilistic forecasts without requiring any explicit post-processing reconciliation. Unlike the state-of-the-art, the proposed method simultaneously learns from all time series in the hierarchy and incorporates the r... | [] | null | 805 | null | null |
v139/rao21a | MSA Transformer | https://proceedings.mlr.press/v139/rao21a.html | [
"Roshan M Rao",
"Jason Liu",
"Robert Verkuil",
"Joshua Meier",
"John Canny",
"Pieter Abbeel",
"Tom Sercu",
"Alexander Rives"
] | null | null | Unsupervised protein language models trained across millions of diverse sequences learn structure and function of proteins. Protein language models studied to date have been trained to perform inference from individual sequences. The longstanding approach in computational biology has been to make inferences from a fami... | [] | null | 806 | null | null |
v139/rasul21a | Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting | https://proceedings.mlr.press/v139/rasul21a.html | [
"Kashif Rasul",
"Calvin Seward",
"Ingmar Schuster",
"Roland Vollgraf"
] | null | null | In this work, we propose TimeGrad, an autoregressive model for multivariate probabilistic time series forecasting which samples from the data distribution at each time step by estimating its gradient. To this end, we use diffusion probabilistic models, a class of latent variable models closely connected to score matchi... | [] | null | 807 | 2101.12072 | title_snapshot |
v139/ratzlaff21a | Generative Particle Variational Inference via Estimation of Functional Gradients | https://proceedings.mlr.press/v139/ratzlaff21a.html | [
"Neale Ratzlaff",
"Qinxun Bai",
"Li Fuxin",
"Wei Xu"
] | null | null | Recently, particle-based variational inference (ParVI) methods have gained interest because they can avoid arbitrary parametric assumptions that are common in variational inference. However, many ParVI approaches do not allow arbitrary sampling from the posterior, and the few that do allow such sampling suffer from sub... | [] | null | 808 | 2103.01291 | title_snapshot |
v139/raviv21a | Enhancing Robustness of Neural Networks through Fourier Stabilization | https://proceedings.mlr.press/v139/raviv21a.html | [
"Netanel Raviv",
"Aidan Kelley",
"Minzhe Guo",
"Yevgeniy Vorobeychik"
] | null | null | Despite the considerable success of neural networks in security settings such as malware detection, such models have proved vulnerable to evasion attacks, in which attackers make slight changes to inputs (e.g., malware) to bypass detection. We propose a novel approach, Fourier stabilization, for designing evasion-robus... | [] | null | 809 | 2106.04435 | title_snapshot |
v139/rawat21a | Disentangling Sampling and Labeling Bias for Learning in Large-output Spaces | https://proceedings.mlr.press/v139/rawat21a.html | [
"Ankit Singh Rawat",
"Aditya K Menon",
"Wittawat Jitkrittum",
"Sadeep Jayasumana",
"Felix Yu",
"Sashank Reddi",
"Sanjiv Kumar"
] | null | null | Negative sampling schemes enable efficient training given a large number of classes, by offering a means to approximate a computationally expensive loss function that takes all labels into account. In this paper, we present a new connection between these schemes and loss modification techniques for countering label imb... | [] | null | 810 | 2105.05736 | title_snapshot |
v139/raychaudhuri21a | Cross-domain Imitation from Observations | https://proceedings.mlr.press/v139/raychaudhuri21a.html | [
"Dripta S. Raychaudhuri",
"Sujoy Paul",
"Jeroen Vanbaar",
"Amit K. Roy-Chowdhury"
] | null | null | Imitation learning seeks to circumvent the difficulty in designing proper reward functions for training agents by utilizing expert behavior. With environments modeled as Markov Decision Processes (MDP), most of the existing imitation algorithms are contingent on the availability of expert demonstrations in the same MDP... | [] | null | 811 | 2105.10037 | title_snapshot |
v139/razin21a | Implicit Regularization in Tensor Factorization | https://proceedings.mlr.press/v139/razin21a.html | [
"Noam Razin",
"Asaf Maman",
"Nadav Cohen"
] | null | null | Recent efforts to unravel the mystery of implicit regularization in deep learning have led to a theoretical focus on matrix factorization — matrix completion via linear neural network. As a step further towards practical deep learning, we provide the first theoretical analysis of implicit regularization in tensor facto... | [] | null | 812 | 2102.09972 | title_snapshot |
v139/refinetti21a | Align, then memorise: the dynamics of learning with feedback alignment | https://proceedings.mlr.press/v139/refinetti21a.html | [
"Maria Refinetti",
"Stéphane D’Ascoli",
"Ruben Ohana",
"Sebastian Goldt"
] | null | null | Direct Feedback Alignment (DFA) is emerging as an efficient and biologically plausible alternative to backpropagation for training deep neural networks. Despite relying on random feedback weights for the backward pass, DFA successfully trains state-of-the-art models such as Transformers. On the other hand, it notorious... | [] | null | 813 | 2011.12428 | title_snapshot |
v139/refinetti21b | Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed | https://proceedings.mlr.press/v139/refinetti21b.html | [
"Maria Refinetti",
"Sebastian Goldt",
"Florent Krzakala",
"Lenka Zdeborova"
] | null | null | A recent series of theoretical works showed that the dynamics of neural networks with a certain initialisation are well-captured by kernel methods. Concurrent empirical work demonstrated that kernel methods can come close to the performance of neural networks on some image classification tasks. These results raise the ... | [] | null | 814 | 2102.11742 | title_snapshot |
v139/rematas21a | Sharf: Shape-conditioned Radiance Fields from a Single View | https://proceedings.mlr.press/v139/rematas21a.html | [
"Konstantinos Rematas",
"Ricardo Martin-Brualla",
"Vittorio Ferrari"
] | null | null | We present a method for estimating neural scenes representations of objects given only a single image. The core of our method is the estimation of a geometric scaffold for the object and its use as a guide for the reconstruction of the underlying radiance field. Our formulation is based on a generative process that fir... | [] | null | 815 | 2102.08860 | title_snapshot |
v139/ren21a | LEGO: Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs | https://proceedings.mlr.press/v139/ren21a.html | [
"Hongyu Ren",
"Hanjun Dai",
"Bo Dai",
"Xinyun Chen",
"Michihiro Yasunaga",
"Haitian Sun",
"Dale Schuurmans",
"Jure Leskovec",
"Denny Zhou"
] | null | null | Answering complex natural language questions on knowledge graphs (KGQA) is a challenging task. It requires reasoning with the input natural language questions as well as a massive, incomplete heterogeneous KG. Prior methods obtain an abstract structured query graph/tree from the input question and traverse the KG for a... | [] | null | 816 | null | null |
v139/ren21b | Interpreting and Disentangling Feature Components of Various Complexity from DNNs | https://proceedings.mlr.press/v139/ren21b.html | [
"Jie Ren",
"Mingjie Li",
"Zexu Liu",
"Quanshi Zhang"
] | null | null | This paper aims to define, visualize, and analyze the feature complexity that is learned by a DNN. We propose a generic definition for the feature complexity. Given the feature of a certain layer in the DNN, our method decomposes and visualizes feature components of different complexity orders from the feature. The fea... | [] | null | 817 | 2006.15920 | title_snapshot |
v139/ren21c | Integrated Defense for Resilient Graph Matching | https://proceedings.mlr.press/v139/ren21c.html | [
"Jiaxiang Ren",
"Zijie Zhang",
"Jiayin Jin",
"Xin Zhao",
"Sixing Wu",
"Yang Zhou",
"Yelong Shen",
"Tianshi Che",
"Ruoming Jin",
"Dejing Dou"
] | null | null | A recent study has shown that graph matching models are vulnerable to adversarial manipulation of their input which is intended to cause a mismatching. Nevertheless, there is still a lack of a comprehensive solution for further enhancing the robustness of graph matching against adversarial attacks. In this paper, we id... | [] | null | 818 | null | null |
v139/richter21a | Solving high-dimensional parabolic PDEs using the tensor train format | https://proceedings.mlr.press/v139/richter21a.html | [
"Lorenz Richter",
"Leon Sallandt",
"Nikolas Nüsken"
] | null | null | High-dimensional partial differential equations (PDEs) are ubiquitous in economics, science and engineering. However, their numerical treatment poses formidable challenges since traditional grid-based methods tend to be frustrated by the curse of dimensionality. In this paper, we argue that tensor trains provide an app... | [] | null | 819 | 2102.11830 | title_snapshot |
v139/rizk21a | Best Arm Identification in Graphical Bilinear Bandits | https://proceedings.mlr.press/v139/rizk21a.html | [
"Geovani Rizk",
"Albert Thomas",
"Igor Colin",
"Rida Laraki",
"Yann Chevaleyre"
] | null | null | We introduce a new graphical bilinear bandit problem where a learner (or a \emph{central entity}) allocates arms to the nodes of a graph and observes for each edge a noisy bilinear reward representing the interaction between the two end nodes. We study the best arm identification problem in which the learner wants to f... | [] | null | 820 | 2012.07641 | title_snapshot |
v139/roddenberry21a | Principled Simplicial Neural Networks for Trajectory Prediction | https://proceedings.mlr.press/v139/roddenberry21a.html | [
"T. Mitchell Roddenberry",
"Nicholas Glaze",
"Santiago Segarra"
] | null | null | We consider the construction of neural network architectures for data on simplicial complexes. In studying maps on the chain complex of a simplicial complex, we define three desirable properties of a simplicial neural network architecture: namely, permutation equivariance, orientation equivariance, and simplicial aware... | [] | null | 821 | 2102.10058 | title_snapshot |
v139/roeder21a | On Linear Identifiability of Learned Representations | https://proceedings.mlr.press/v139/roeder21a.html | [
"Geoffrey Roeder",
"Luke Metz",
"Durk Kingma"
] | null | null | Identifiability is a desirable property of a statistical model: it implies that the true model parameters may be estimated to any desired precision, given sufficient computational resources and data. We study identifiability in the context of representation learning: discovering nonlinear data representations that are ... | [] | null | 822 | 2007.00810 | title_snapshot |
v139/rolf21a | Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data | https://proceedings.mlr.press/v139/rolf21a.html | [
"Esther Rolf",
"Theodora T Worledge",
"Benjamin Recht",
"Michael Jordan"
] | null | null | Collecting more diverse and representative training data is often touted as a remedy for the disparate performance of machine learning predictors across subpopulations. However, a precise framework for understanding how dataset properties like diversity affect learning outcomes is largely lacking. By casting data colle... | [] | null | 823 | 2103.03399 | title_snapshot |
v139/romac21a | TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL | https://proceedings.mlr.press/v139/romac21a.html | [
"Clément Romac",
"Rémy Portelas",
"Katja Hofmann",
"Pierre-Yves Oudeyer"
] | null | null | Training autonomous agents able to generalize to multiple tasks is a key target of Deep Reinforcement Learning (DRL) research. In parallel to improving DRL algorithms themselves, Automatic Curriculum Learning (ACL) study how teacher algorithms can train DRL agents more efficiently by adapting task selection to their ev... | [] | null | 824 | 2103.09815 | title_snapshot |
v139/rosca21a | Discretization Drift in Two-Player Games | https://proceedings.mlr.press/v139/rosca21a.html | [
"Mihaela C Rosca",
"Yan Wu",
"Benoit Dherin",
"David Barrett"
] | null | null | Gradient-based methods for two-player games produce rich dynamics that can solve challenging problems, yet can be difficult to stabilize and understand. Part of this complexity originates from the discrete update steps given by simultaneous or alternating gradient descent, which causes each player to drift away from th... | [] | null | 825 | 2105.13922 | title_snapshot |
v139/rosenfeld21a | On the Predictability of Pruning Across Scales | https://proceedings.mlr.press/v139/rosenfeld21a.html | [
"Jonathan S Rosenfeld",
"Jonathan Frankle",
"Michael Carbin",
"Nir Shavit"
] | null | null | We show that the error of iteratively magnitude-pruned networks empirically follows a scaling law with interpretable coefficients that depend on the architecture and task. We functionally approximate the error of the pruned networks, showing it is predictable in terms of an invariant tying width, depth, and pruning lev... | [] | null | 826 | 2006.10621 | title_snapshot |
v139/ross21a | Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement | https://proceedings.mlr.press/v139/ross21a.html | [
"Andrew Ross",
"Finale Doshi-Velez"
] | null | null | In representation learning, there has been recent interest in developing algorithms to disentangle the ground-truth generative factors behind a dataset, and metrics to quantify how fully this occurs. However, these algorithms and metrics often assume that both representations and ground-truth factors are flat, continuo... | [] | null | 827 | 2102.05185 | title_snapshot |
v139/roth21a | Simultaneous Similarity-based Self-Distillation for Deep Metric Learning | https://proceedings.mlr.press/v139/roth21a.html | [
"Karsten Roth",
"Timo Milbich",
"Bjorn Ommer",
"Joseph Paul Cohen",
"Marzyeh Ghassemi"
] | null | null | Deep Metric Learning (DML) provides a crucial tool for visual similarity and zero-shot retrieval applications by learning generalizing embedding spaces, although recent work in DML has shown strong performance saturation across training objectives. However, generalization capacity is known to scale with the embedding s... | [] | null | 828 | 2009.08348 | title_judge |
v139/rothblum21a | Multi-group Agnostic PAC Learnability | https://proceedings.mlr.press/v139/rothblum21a.html | [
"Guy N Rothblum",
"Gal Yona"
] | null | null | An agnostic PAC learning algorithm finds a predictor that is competitive with the best predictor in a benchmark hypothesis class, where competitiveness is measured with respect to a given loss function. However, its predictions might be quite sub-optimal for structured subgroups of individuals, such as protected demogr... | [] | null | 829 | 2105.09989 | title_snapshot |
v139/rothfuss21a | PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees | https://proceedings.mlr.press/v139/rothfuss21a.html | [
"Jonas Rothfuss",
"Vincent Fortuin",
"Martin Josifoski",
"Andreas Krause"
] | null | null | Meta-learning can successfully acquire useful inductive biases from data. Yet, its generalization properties to unseen learning tasks are poorly understood. Particularly if the number of meta-training tasks is small, this raises concerns about overfitting. We provide a theoretical analysis using the PAC-Bayesian framew... | [] | null | 830 | 2002.05551 | title_snapshot |
v139/rouyer21a | An Algorithm for Stochastic and Adversarial Bandits with Switching Costs | https://proceedings.mlr.press/v139/rouyer21a.html | [
"Chloé Rouyer",
"Yevgeny Seldin",
"Nicolò Cesa-Bianchi"
] | null | null | We propose an algorithm for stochastic and adversarial multiarmed bandits with switching costs, where the algorithm pays a price $\lambda$ every time it switches the arm being played. Our algorithm is based on adaptation of the Tsallis-INF algorithm of Zimmert and Seldin (2021) and requires no prior knowledge of the re... | [] | null | 831 | 2102.09864 | title_snapshot |
v139/ruan21a | Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding | https://proceedings.mlr.press/v139/ruan21a.html | [
"Yangjun Ruan",
"Karen Ullrich",
"Daniel S Severo",
"James Townsend",
"Ashish Khisti",
"Arnaud Doucet",
"Alireza Makhzani",
"Chris Maddison"
] | null | null | Latent variable models have been successfully applied in lossless compression with the bits-back coding algorithm. However, bits-back suffers from an increase in the bitrate equal to the KL divergence between the approximate posterior and the true posterior. In this paper, we show how to remove this gap asymptotically ... | [] | null | 832 | 2102.11086 | title_snapshot |
v139/rudner21a | On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes | https://proceedings.mlr.press/v139/rudner21a.html | [
"Tim G. J. Rudner",
"Oscar Key",
"Yarin Gal",
"Tom Rainforth"
] | null | null | We show that the gradient estimates used in training Deep Gaussian Processes (DGPs) with importance-weighted variational inference are susceptible to signal-to-noise ratio (SNR) issues. Specifically, we show both theoretically and via an extensive empirical evaluation that the SNR of the gradient estimates for the late... | [] | null | 833 | 2011.00515 | title_snapshot |
v139/ruiz-garcia21a | Tilting the playing field: Dynamical loss functions for machine learning | https://proceedings.mlr.press/v139/ruiz-garcia21a.html | [
"Miguel Ruiz-Garcia",
"Ge Zhang",
"Samuel S Schoenholz",
"Andrea J. Liu"
] | null | null | We show that learning can be improved by using loss functions that evolve cyclically during training to emphasize one class at a time. In underparameterized networks, such dynamical loss functions can lead to successful training for networks that fail to find deep minima of the standard cross-entropy loss. In overparam... | [] | null | 834 | 2102.03793 | title_snapshot |
v139/rusch21a | UnICORNN: A recurrent model for learning very long time dependencies | https://proceedings.mlr.press/v139/rusch21a.html | [
"T. Konstantin Rusch",
"Siddhartha Mishra"
] | null | null | The design of recurrent neural networks (RNNs) to accurately process sequential inputs with long-time dependencies is very challenging on account of the exploding and vanishing gradient problem. To overcome this, we propose a novel RNN architecture which is based on a structure preserving discretization of a Hamiltonia... | [] | null | 835 | 2103.05487 | title_snapshot |
v139/rybkin21a | Simple and Effective VAE Training with Calibrated Decoders | https://proceedings.mlr.press/v139/rybkin21a.html | [
"Oleh Rybkin",
"Kostas Daniilidis",
"Sergey Levine"
] | null | null | Variational autoencoders (VAEs) provide an effective and simple method for modeling complex distributions. However, training VAEs often requires considerable hyperparameter tuning to determine the optimal amount of information retained by the latent variable. We study the impact of calibrated decoders, which learn the ... | [] | null | 836 | 2006.13202 | title_snapshot |
v139/rybkin21b | Model-Based Reinforcement Learning via Latent-Space Collocation | https://proceedings.mlr.press/v139/rybkin21b.html | [
"Oleh Rybkin",
"Chuning Zhu",
"Anusha Nagabandi",
"Kostas Daniilidis",
"Igor Mordatch",
"Sergey Levine"
] | null | null | The ability to plan into the future while utilizing only raw high-dimensional observations, such as images, can provide autonomous agents with broad and general capabilities. However, realistic tasks require performing temporally extended reasoning, and cannot be solved with only myopic, short-sighted planning. Recent ... | [] | null | 837 | 2106.13229 | title_snapshot |
v139/s21a | Training Data Subset Selection for Regression with Controlled Generalization Error | https://proceedings.mlr.press/v139/s21a.html | [
"Durga S",
"Rishabh Iyer",
"Ganesh Ramakrishnan",
"Abir De"
] | null | null | Data subset selection from a large number of training instances has been a successful approach toward efficient and cost-effective machine learning. However, models trained on a smaller subset may show poor generalization ability. In this paper, our goal is to design an algorithm for selecting a subset of the training ... | [] | null | 838 | 2106.12491 | title_snapshot |
v139/sabour21a | Unsupervised Part Representation by Flow Capsules | https://proceedings.mlr.press/v139/sabour21a.html | [
"Sara Sabour",
"Andrea Tagliasacchi",
"Soroosh Yazdani",
"Geoffrey Hinton",
"David J Fleet"
] | null | null | Capsule networks aim to parse images into a hierarchy of objects, parts and relations. While promising, they remain limited by an inability to learn effective low level part descriptions. To address this issue we propose a way to learn primary capsule encoders that detect atomic parts from a single image. During traini... | [] | null | 839 | 2011.13920 | title_snapshot |
v139/safaryan21a | Stochastic Sign Descent Methods: New Algorithms and Better Theory | https://proceedings.mlr.press/v139/safaryan21a.html | [
"Mher Safaryan",
"Peter Richtarik"
] | null | null | Various gradient compression schemes have been proposed to mitigate the communication cost in distributed training of large scale machine learning models. Sign-based methods, such as signSGD (Bernstein et al., 2018), have recently been gaining popularity because of their simple compression rule and connection to adapti... | [] | null | 840 | 1905.12938 | title_snapshot |
v139/saha21a | Adversarial Dueling Bandits | https://proceedings.mlr.press/v139/saha21a.html | [
"Aadirupa Saha",
"Tomer Koren",
"Yishay Mansour"
] | null | null | We introduce the problem of regret minimization in Adversarial Dueling Bandits. As in classic Dueling Bandits, the learner has to repeatedly choose a pair of items and observe only a relative binary ‘win-loss’ feedback for this pair, but here this feedback is generated from an arbitrary preference matrix, possibly chos... | [] | null | 841 | 2010.14563 | title_snapshot |
v139/saha21b | Dueling Convex Optimization | https://proceedings.mlr.press/v139/saha21b.html | [
"Aadirupa Saha",
"Tomer Koren",
"Yishay Mansour"
] | null | null | We address the problem of convex optimization with preference (dueling) feedback. Like the traditional optimization objective, the goal is to find the optimal point with the least possible query complexity, however, without the luxury of even a zeroth order feedback. Instead, the learner can only observe a single noisy... | [] | null | 842 | null | null |
v139/saha21c | Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization | https://proceedings.mlr.press/v139/saha21c.html | [
"Aadirupa Saha",
"Nagarajan Natarajan",
"Praneeth Netrapalli",
"Prateek Jain"
] | null | null | We study online learning with bandit feedback (i.e. learner has access to only zeroth-order oracle) where cost/reward functions $\f_t$ admit a "pseudo-1d" structure, i.e. $\f_t(\w) = \loss_t(\pred_t(\w))$ where the output of $\pred_t$ is one-dimensional. At each round, the learner observes context $\x_t$, plays predict... | [] | null | 843 | 2102.07387 | title_snapshot |
v139/sahraee-ardakan21a | Asymptotics of Ridge Regression in Convolutional Models | https://proceedings.mlr.press/v139/sahraee-ardakan21a.html | [
"Mojtaba Sahraee-Ardakan",
"Tung Mai",
"Anup Rao",
"Ryan A. Rossi",
"Sundeep Rangan",
"Alyson K Fletcher"
] | null | null | Understanding generalization and estimation error of estimators for simple models such as linear and generalized linear models has attracted a lot of attention recently. This is in part due to an interesting observation made in machine learning community that highly over-parameterized neural networks achieve zero train... | [] | null | 844 | 2103.04557 | title_snapshot |
v139/sander21a | Momentum Residual Neural Networks | https://proceedings.mlr.press/v139/sander21a.html | [
"Michael E. Sander",
"Pierre Ablin",
"Mathieu Blondel",
"Gabriel Peyré"
] | null | null | The training of deep residual neural networks (ResNets) with backpropagation has a memory cost that increases linearly with respect to the depth of the network. A simple way to circumvent this issue is to use reversible architectures. In this paper, we propose to change the forward rule of a ResNet by adding a momentum... | [] | null | 845 | 2102.07870 | title_snapshot |
v139/sandler21a | Meta-Learning Bidirectional Update Rules | https://proceedings.mlr.press/v139/sandler21a.html | [
"Mark Sandler",
"Max Vladymyrov",
"Andrey Zhmoginov",
"Nolan Miller",
"Tom Madams",
"Andrew Jackson",
"Blaise Agüera Y Arcas"
] | null | null | In this paper, we introduce a new type of generalized neural network where neurons and synapses maintain multiple states. We show that classical gradient-based backpropagation in neural networks can be seen as a special case of a two-state network where one state is used for activations and another for gradients, with ... | [] | null | 846 | 2104.04657 | title_snapshot |
v139/sarafian21a | Recomposing the Reinforcement Learning Building Blocks with Hypernetworks | https://proceedings.mlr.press/v139/sarafian21a.html | [
"Elad Sarafian",
"Shai Keynan",
"Sarit Kraus"
] | null | null | The Reinforcement Learning (RL) building blocks, i.e. $Q$-functions and policy networks, usually take elements from the cartesian product of two domains as input. In particular, the input of the $Q$-function is both the state and the action, and in multi-task problems (Meta-RL) the policy can take a state and a context... | [] | null | 847 | 2106.06842 | title_snapshot |
v139/sarussi21a | Towards Understanding Learning in Neural Networks with Linear Teachers | https://proceedings.mlr.press/v139/sarussi21a.html | [
"Roei Sarussi",
"Alon Brutzkus",
"Amir Globerson"
] | null | null | Can a neural network minimizing cross-entropy learn linearly separable data? Despite progress in the theory of deep learning, this question remains unsolved. Here we prove that SGD globally optimizes this learning problem for a two-layer network with Leaky ReLU activations. The learned network can in principle be very ... | [] | null | 848 | 2101.02533 | title_snapshot |
v139/satorras21a | E(n) Equivariant Graph Neural Networks | https://proceedings.mlr.press/v139/satorras21a.html | [
"Vı́ctor Garcia Satorras",
"Emiel Hoogeboom",
"Max Welling"
] | null | null | This paper introduces a new model to learn graph neural networks equivariant to rotations, translations, reflections and permutations called E(n)-Equivariant Graph Neural Networks (EGNNs). In contrast with existing methods, our work does not require computationally expensive higher-order representations in intermediate... | [] | null | 849 | 2102.09844 | title_snapshot |
v139/saunshi21a | A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-Learning | https://proceedings.mlr.press/v139/saunshi21a.html | [
"Nikunj Saunshi",
"Arushi Gupta",
"Wei Hu"
] | null | null | An effective approach in meta-learning is to utilize multiple “train tasks” to learn a good initialization for model parameters that can help solve unseen “test tasks” with very few samples by fine-tuning from this initialization. Although successful in practice, theoretical understanding of such methods is limited. Th... | [] | null | 850 | 2106.15615 | title_snapshot |
v139/scetbon21a | Low-Rank Sinkhorn Factorization | https://proceedings.mlr.press/v139/scetbon21a.html | [
"Meyer Scetbon",
"Marco Cuturi",
"Gabriel Peyré"
] | null | null | Several recent applications of optimal transport (OT) theory to machine learning have relied on regularization, notably entropy and the Sinkhorn algorithm. Because matrix-vector products are pervasive in the Sinkhorn algorithm, several works have proposed to \textit{approximate} kernel matrices appearing in its iterati... | [] | null | 851 | 2103.04737 | title_snapshot |
v139/schlag21a | Linear Transformers Are Secretly Fast Weight Programmers | https://proceedings.mlr.press/v139/schlag21a.html | [
"Imanol Schlag",
"Kazuki Irie",
"Jürgen Schmidhuber"
] | null | null | We show the formal equivalence of linearised self-attention mechanisms and fast weight controllers from the early ’90s, where a slow neural net learns by gradient descent to program the fast weights of another net through sequences of elementary programming instructions which are additive outer products of self-invente... | [] | null | 852 | 2102.11174 | title_snapshot |
v139/schmidt21a | Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers | https://proceedings.mlr.press/v139/schmidt21a.html | [
"Robin M Schmidt",
"Frank Schneider",
"Philipp Hennig"
] | null | null | Choosing the optimizer is considered to be among the most crucial design decisions in deep learning, and it is not an easy one. The growing literature now lists hundreds of optimization methods. In the absence of clear theoretical guidance and conclusive empirical evidence, the decision is often made based on anecdotes... | [] | null | 853 | 2007.01547 | title_snapshot |
v139/schutt21a | Equivariant message passing for the prediction of tensorial properties and molecular spectra | https://proceedings.mlr.press/v139/schutt21a.html | [
"Kristof Schütt",
"Oliver Unke",
"Michael Gastegger"
] | null | null | Message passing neural networks have become a method of choice for learning on graphs, in particular the prediction of chemical properties and the acceleration of molecular dynamics studies. While they readily scale to large training data sets, previous approaches have proven to be less data efficient than kernel metho... | [] | null | 854 | 2102.03150 | title_snapshot |
v139/schwarzschild21a | Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks | https://proceedings.mlr.press/v139/schwarzschild21a.html | [
"Avi Schwarzschild",
"Micah Goldblum",
"Arjun Gupta",
"John P Dickerson",
"Tom Goldstein"
] | null | null | Data poisoning and backdoor attacks manipulate training data in order to cause models to fail during inference. A recent survey of industry practitioners found that data poisoning is the number one concern among threats ranging from model stealing to adversarial attacks. However, it remains unclear exactly how dangerou... | [] | null | 855 | 2006.12557 | title_snapshot |
v139/scieur21a | Connecting Sphere Manifolds Hierarchically for Regularization | https://proceedings.mlr.press/v139/scieur21a.html | [
"Damien Scieur",
"Youngsung Kim"
] | null | null | This paper considers classification problems with hierarchically organized classes. We force the classifier (hyperplane) of each class to belong to a sphere manifold, whose center is the classifier of its super-class. Then, individual sphere manifolds are connected based on their hierarchical relations. Our technique r... | [] | null | 856 | 2106.13549 | title_snapshot |
v139/seidenschwarz21a | Learning Intra-Batch Connections for Deep Metric Learning | https://proceedings.mlr.press/v139/seidenschwarz21a.html | [
"Jenny Denise Seidenschwarz",
"Ismail Elezi",
"Laura Leal-Taixé"
] | null | null | The goal of metric learning is to learn a function that maps samples to a lower-dimensional space where similar samples lie closer than dissimilar ones. Particularly, deep metric learning utilizes neural networks to learn such a mapping. Most approaches rely on losses that only take the relations between pairs or tripl... | [] | null | 857 | 2102.07753 | title_snapshot |
v139/sen21a | Top-k eXtreme Contextual Bandits with Arm Hierarchy | https://proceedings.mlr.press/v139/sen21a.html | [
"Rajat Sen",
"Alexander Rakhlin",
"Lexing Ying",
"Rahul Kidambi",
"Dean Foster",
"Daniel N Hill",
"Inderjit S. Dhillon"
] | null | null | Motivated by modern applications, such as online advertisement and recommender systems, we study the top-$k$ extreme contextual bandits problem, where the total number of arms can be enormous, and the learner is allowed to select $k$ arms and observe all or some of the rewards for the chosen arms. We first propose an a... | [] | null | 858 | 2102.07800 | title_snapshot |
v139/sentenac21a | Pure Exploration and Regret Minimization in Matching Bandits | https://proceedings.mlr.press/v139/sentenac21a.html | [
"Flore Sentenac",
"Jialin Yi",
"Clement Calauzenes",
"Vianney Perchet",
"Milan Vojnovic"
] | null | null | Finding an optimal matching in a weighted graph is a standard combinatorial problem. We consider its semi-bandit version where either a pair or a full matching is sampled sequentially. We prove that it is possible to leverage a rank-1 assumption on the adjacency matrix to reduce the sample complexity and the regret of ... | [] | null | 859 | 2108.00230 | title_snapshot |
v139/seo21a | State Entropy Maximization with Random Encoders for Efficient Exploration | https://proceedings.mlr.press/v139/seo21a.html | [
"Younggyo Seo",
"Lili Chen",
"Jinwoo Shin",
"Honglak Lee",
"Pieter Abbeel",
"Kimin Lee"
] | null | null | Recent exploration methods have proven to be a recipe for improving sample-efficiency in deep reinforcement learning (RL). However, efficient exploration in high-dimensional observation spaces still remains a challenge. This paper presents Random Encoders for Efficient Exploration (RE3), an exploration method that util... | [] | null | 860 | 2102.09430 | title_snapshot |
v139/sessa21a | Online Submodular Resource Allocation with Applications to Rebalancing Shared Mobility Systems | https://proceedings.mlr.press/v139/sessa21a.html | [
"Pier Giuseppe Sessa",
"Ilija Bogunovic",
"Andreas Krause",
"Maryam Kamgarpour"
] | null | null | Motivated by applications in shared mobility, we address the problem of allocating a group of agents to a set of resources to maximize a cumulative welfare objective. We model the welfare obtainable from each resource as a monotone DR-submodular function which is a-priori unknown and can only be learned by observing th... | [] | null | 861 | null | null |
v139/shah21a | RRL: Resnet as representation for Reinforcement Learning | https://proceedings.mlr.press/v139/shah21a.html | [
"Rutav M Shah",
"Vikash Kumar"
] | null | null | The ability to autonomously learn behaviors via direct interactions in uninstrumented environments can lead to generalist robots capable of enhancing productivity or providing care in unstructured settings like homes. Such uninstrumented settings warrant operations only using the robot’s proprioceptive sensor such as o... | [] | null | 862 | 2107.03380 | title_snapshot |
v139/shakerinava21a | Equivariant Networks for Pixelized Spheres | https://proceedings.mlr.press/v139/shakerinava21a.html | [
"Mehran Shakerinava",
"Siamak Ravanbakhsh"
] | null | null | Pixelizations of Platonic solids such as the cube and icosahedron have been widely used to represent spherical data, from climate records to Cosmic Microwave Background maps. Platonic solids have well-known global symmetries. Once we pixelize each face of the solid, each face also possesses its own local symmetries in ... | [] | null | 863 | 2106.06662 | title_snapshot |
v139/shamsian21a | Personalized Federated Learning using Hypernetworks | https://proceedings.mlr.press/v139/shamsian21a.html | [
"Aviv Shamsian",
"Aviv Navon",
"Ethan Fetaya",
"Gal Chechik"
] | null | null | Personalized federated learning is tasked with training machine learning models for multiple clients, each with its own data distribution. The goal is to train personalized models collaboratively while accounting for data disparities across clients and reducing communication costs. We propose a novel approach to this p... | [] | null | 864 | 2103.04628 | title_snapshot |
v139/shen21a | On the Power of Localized Perceptron for Label-Optimal Learning of Halfspaces with Adversarial Noise | https://proceedings.mlr.press/v139/shen21a.html | [
"Jie Shen"
] | null | null | We study {\em online} active learning of homogeneous halfspaces in $\mathbb{R}^d$ with adversarial noise where the overall probability of a noisy label is constrained to be at most $\nu$. Our main contribution is a Perceptron-like online active learning algorithm that runs in polynomial time, and under the conditions t... | [] | null | 865 | 2012.10793 | title_snapshot |
v139/shen21b | Sample-Optimal PAC Learning of Halfspaces with Malicious Noise | https://proceedings.mlr.press/v139/shen21b.html | [
"Jie Shen"
] | null | null | We study efficient PAC learning of homogeneous halfspaces in $\mathbb{R}^d$ in the presence of malicious noise of Valiant (1985). This is a challenging noise model and only until recently has near-optimal noise tolerance bound been established under the mild condition that the unlabeled data distribution is isotropic l... | [] | null | 866 | 2102.06247 | title_snapshot |
v139/shen21c | Backdoor Scanning for Deep Neural Networks through K-Arm Optimization | https://proceedings.mlr.press/v139/shen21c.html | [
"Guangyu Shen",
"Yingqi Liu",
"Guanhong Tao",
"Shengwei An",
"Qiuling Xu",
"Siyuan Cheng",
"Shiqing Ma",
"Xiangyu Zhang"
] | null | null | Back-door attack poses a severe threat to deep learning systems. It injects hidden malicious behaviors to a model such that any input stamped with a special pattern can trigger such behaviors. Detecting back-door is hence of pressing need. Many existing defense techniques use optimization to generate the smallest input... | [] | null | 867 | 2102.05123 | title_snapshot |
v139/shen21d | State Relevance for Off-Policy Evaluation | https://proceedings.mlr.press/v139/shen21d.html | [
"Simon P Shen",
"Yecheng Ma",
"Omer Gottesman",
"Finale Doshi-Velez"
] | null | null | Importance sampling-based estimators for off-policy evaluation (OPE) are valued for their simplicity, unbiasedness, and reliance on relatively few assumptions. However, the variance of these estimators is often high, especially when trajectories are of different lengths. In this work, we introduce Omitting-States-Irrel... | [] | null | 868 | 2109.06310 | title_snapshot |
v139/shi21a | SparseBERT: Rethinking the Importance Analysis in Self-attention | https://proceedings.mlr.press/v139/shi21a.html | [
"Han Shi",
"Jiahui Gao",
"Xiaozhe Ren",
"Hang Xu",
"Xiaodan Liang",
"Zhenguo Li",
"James Tin-Yau Kwok"
] | null | null | Transformer-based models are popularly used in natural language processing (NLP). Its core component, self-attention, has aroused widespread interest. To understand the self-attention mechanism, a direct method is to visualize the attention map of a pre-trained model. Based on the patterns observed, a series of efficie... | [] | null | 869 | 2102.12871 | title_snapshot |
v139/shi21b | Learning Gradient Fields for Molecular Conformation Generation | https://proceedings.mlr.press/v139/shi21b.html | [
"Chence Shi",
"Shitong Luo",
"Minkai Xu",
"Jian Tang"
] | null | null | We study a fundamental problem in computational chemistry known as molecular conformation generation, trying to predict stable 3D structures from 2D molecular graphs. Existing machine learning approaches usually first predict distances between atoms and then generate a 3D structure satisfying the distances, where noise... | [] | null | 870 | 2105.03902 | title_snapshot |
v139/shi21c | Segmenting Hybrid Trajectories using Latent ODEs | https://proceedings.mlr.press/v139/shi21c.html | [
"Ruian Shi",
"Quaid Morris"
] | null | null | Smooth dynamics interrupted by discontinuities are known as hybrid systems and arise commonly in nature. Latent ODEs allow for powerful representation of irregularly sampled time series but are not designed to capture trajectories arising from hybrid systems. Here, we propose the Latent Segmented ODE (LatSegODE), which... | [] | null | 871 | 2105.03835 | title_snapshot |
v139/shi21d | Deeply-Debiased Off-Policy Interval Estimation | https://proceedings.mlr.press/v139/shi21d.html | [
"Chengchun Shi",
"Runzhe Wan",
"Victor Chernozhukov",
"Rui Song"
] | null | null | Off-policy evaluation learns a target policy’s value with a historical dataset generated by a different behavior policy. In addition to a point estimate, many applications would benefit significantly from having a confidence interval (CI) that quantifies the uncertainty of the point estimate. In this paper, we propose ... | [] | null | 872 | 2105.04646 | title_snapshot |
v139/shih21a | GANMEX: One-vs-One Attributions using GAN-based Model Explainability | https://proceedings.mlr.press/v139/shih21a.html | [
"Sheng-Min Shih",
"Pin-Ju Tien",
"Zohar Karnin"
] | null | null | Attribution methods have been shown as promising approaches for identifying key features that led to learned model predictions. While most existing attribution methods rely on a baseline input for performing feature perturbations, limited research has been conducted to address the baseline selection issues. Poor choice... | [] | null | 873 | null | null |
v139/shin21a | Large-Scale Meta-Learning with Continual Trajectory Shifting | https://proceedings.mlr.press/v139/shin21a.html | [
"Jaewoong Shin",
"Hae Beom Lee",
"Boqing Gong",
"Sung Ju Hwang"
] | null | null | Meta-learning of shared initialization parameters has shown to be highly effective in solving few-shot learning tasks. However, extending the framework to many-shot scenarios, which may further enhance its practicality, has been relatively overlooked due to the technical difficulties of meta-learning over long chains o... | [] | null | 874 | 2102.07215 | title_snapshot |
v139/shu21a | AGENT: A Benchmark for Core Psychological Reasoning | https://proceedings.mlr.press/v139/shu21a.html | [
"Tianmin Shu",
"Abhishek Bhandwaldar",
"Chuang Gan",
"Kevin Smith",
"Shari Liu",
"Dan Gutfreund",
"Elizabeth Spelke",
"Joshua Tenenbaum",
"Tomer Ullman"
] | null | null | For machine agents to successfully interact with humans in real-world settings, they will need to develop an understanding of human mental life. Intuitive psychology, the ability to reason about hidden mental variables that drive observable actions, comes naturally to people: even pre-verbal infants can tell agents fro... | [] | null | 875 | 2102.12321 | title_snapshot |
v139/shu21b | Zoo-Tuning: Adaptive Transfer from A Zoo of Models | https://proceedings.mlr.press/v139/shu21b.html | [
"Yang Shu",
"Zhi Kou",
"Zhangjie Cao",
"Jianmin Wang",
"Mingsheng Long"
] | null | null | With the development of deep networks on various large-scale datasets, a large zoo of pretrained models are available. When transferring from a model zoo, applying classic single-model-based transfer learning methods to each source model suffers from high computational cost and cannot fully utilize the rich knowledge i... | [] | null | 876 | 2106.15434 | title_snapshot |
v139/shui21a | Aggregating From Multiple Target-Shifted Sources | https://proceedings.mlr.press/v139/shui21a.html | [
"Changjian Shui",
"Zijian Li",
"Jiaqi Li",
"Christian Gagné",
"Charles X Ling",
"Boyu Wang"
] | null | null | Multi-source domain adaptation aims at leveraging the knowledge from multiple tasks for predicting a related target domain. Hence, a crucial aspect is to properly combine different sources based on their relations. In this paper, we analyzed the problem for aggregating source domains with different label distributions,... | [] | null | 877 | 2105.04051 | title_snapshot |
v139/si21a | Testing Group Fairness via Optimal Transport Projections | https://proceedings.mlr.press/v139/si21a.html | [
"Nian Si",
"Karthyek Murthy",
"Jose Blanchet",
"Viet Anh Nguyen"
] | null | null | We have developed a statistical testing framework to detect if a given machine learning classifier fails to satisfy a wide range of group fairness notions. Our test is a flexible, interpretable, and statistically rigorous tool for auditing whether exhibited biases are intrinsic to the algorithm or simply due to the ran... | [] | null | 878 | 2106.01070 | title_snapshot |
v139/sidheekh21a | On Characterizing GAN Convergence Through Proximal Duality Gap | https://proceedings.mlr.press/v139/sidheekh21a.html | [
"Sahil Sidheekh",
"Aroof Aimen",
"Narayanan C Krishnan"
] | null | null | Despite the accomplishments of Generative Adversarial Networks (GANs) in modeling data distributions, training them remains a challenging task. A contributing factor to this difficulty is the non-intuitive nature of the GAN loss curves, which necessitates a subjective evaluation of the generated output to infer trainin... | [] | null | 879 | 2105.04801 | title_snapshot |
v139/sifaou21a | A Precise Performance Analysis of Support Vector Regression | https://proceedings.mlr.press/v139/sifaou21a.html | [
"Houssem Sifaou",
"Abla Kammoun",
"Mohamed-Slim Alouini"
] | null | null | In this paper, we study the hard and soft support vector regression techniques applied to a set of $n$ linear measurements of the form $y_i=\boldsymbol{\beta}_\star^{T}{\bf x}_i +n_i$ where $\boldsymbol{\beta}_\star$ is an unknown vector, $\left\{{\bf x}_i\right\}_{i=1}^n$ are the feature vectors and $\left\{{n}_i\righ... | [] | null | 880 | 2105.10373 | title_snapshot |
v139/sim21a | Directed Graph Embeddings in Pseudo-Riemannian Manifolds | https://proceedings.mlr.press/v139/sim21a.html | [
"Aaron Sim",
"Maciej L Wiatrak",
"Angus Brayne",
"Paidi Creed",
"Saee Paliwal"
] | null | null | The inductive biases of graph representation learning algorithms are often encoded in the background geometry of their embedding space. In this paper, we show that general directed graphs can be effectively represented by an embedding model that combines three components: a pseudo-Riemannian metric structure, a non-tri... | [] | null | 881 | 2106.08678 | title_snapshot |
v139/sim21b | Collaborative Bayesian Optimization with Fair Regret | https://proceedings.mlr.press/v139/sim21b.html | [
"Rachael Hwee Ling Sim",
"Yehong Zhang",
"Bryan Kian Hsiang Low",
"Patrick Jaillet"
] | null | null | Bayesian optimization (BO) is a popular tool for optimizing complex and costly-to-evaluate black-box objective functions. To further reduce the number of function evaluations, any party performing BO may be interested to collaborate with others to optimize the same objective function concurrently. To do this, existing ... | [] | null | 882 | null | null |
v139/simchi-levi21a | Dynamic Planning and Learning under Recovering Rewards | https://proceedings.mlr.press/v139/simchi-levi21a.html | [
"David Simchi-Levi",
"Zeyu Zheng",
"Feng Zhu"
] | null | null | Motivated by emerging applications such as live-streaming e-commerce, promotions and recommendations, we introduce a general class of multi-armed bandit problems that have the following two features: (i) the decision maker can pull and collect rewards from at most $K$ out of $N$ different arms in each time period; (ii)... | [] | null | 883 | null | null |
v139/simon-gabriel21a | PopSkipJump: Decision-Based Attack for Probabilistic Classifiers | https://proceedings.mlr.press/v139/simon-gabriel21a.html | [
"Carl-Johann Simon-Gabriel",
"Noman Ahmed Sheikh",
"Andreas Krause"
] | null | null | Most current classifiers are vulnerable to adversarial examples, small input perturbations that change the classification output. Many existing attack algorithms cover various settings, from white-box to black-box classifiers, but usually assume that the answers are deterministic and often fail when they are not. We th... | [] | null | 884 | 2106.07445 | title_snapshot |
v139/simsek21a | Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances | https://proceedings.mlr.press/v139/simsek21a.html | [
"Berfin Simsek",
"François Ged",
"Arthur Jacot",
"Francesco Spadaro",
"Clement Hongler",
"Wulfram Gerstner",
"Johanni Brea"
] | null | null | We study how permutation symmetries in overparameterized multi-layer neural networks generate ‘symmetry-induced’ critical points. Assuming a network with $ L $ layers of minimal widths $ r_1^*, \ldots, r_{L-1}^* $ reaches a zero-loss minimum at $ r_1^*! \cdots r_{L-1}^*! $ isolated points that are permutations of one a... | [] | null | 885 | 2105.12221 | title_snapshot |
v139/singal21a | Flow-based Attribution in Graphical Models: A Recursive Shapley Approach | https://proceedings.mlr.press/v139/singal21a.html | [
"Raghav Singal",
"George Michailidis",
"Hoiyi Ng"
] | null | null | We study the attribution problem in a graphical model, wherein the objective is to quantify how the effect of changes at the source nodes propagates through the graph. We develop a model-agnostic flow-based attribution method, called recursive Shapley value (RSV). RSV generalizes a number of existing node-based methods... | [] | null | 886 | null | null |
v139/singh21a | Structured World Belief for Reinforcement Learning in POMDP | https://proceedings.mlr.press/v139/singh21a.html | [
"Gautam Singh",
"Skand Peri",
"Junghyun Kim",
"Hyunseok Kim",
"Sungjin Ahn"
] | null | null | Object-centric world models provide structured representation of the scene and can be an important backbone in reinforcement learning and planning. However, existing approaches suffer in partially-observable environments due to the lack of belief states. In this paper, we propose Structured World Belief, a model for le... | [] | null | 887 | 2107.08577 | title_snapshot |
v139/singla21a | Skew Orthogonal Convolutions | https://proceedings.mlr.press/v139/singla21a.html | [
"Sahil Singla",
"Soheil Feizi"
] | null | null | Training convolutional neural networks with a Lipschitz constraint under the $l_{2}$ norm is useful for provable adversarial robustness, interpretable gradients, stable training, etc. While 1-Lipschitz networks can be designed by imposing a 1-Lipschitz constraint on each layer, training such networks requires each laye... | [] | null | 888 | 2105.11417 | title_snapshot |
v139/sodhani21a | Multi-Task Reinforcement Learning with Context-based Representations | https://proceedings.mlr.press/v139/sodhani21a.html | [
"Shagun Sodhani",
"Amy Zhang",
"Joelle Pineau"
] | null | null | https://drive.google.com/file/d/1lRV72XaKoxZjgQrLXBJhsM82x54_1Vc4/view?usp=sharing | [] | null | 889 | 2102.06177 | title_snapshot |
v139/sohn21a | Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks | https://proceedings.mlr.press/v139/sohn21a.html | [
"Sungryull Sohn",
"Sungtae Lee",
"Jongwook Choi",
"Harm H Van Seijen",
"Mehdi Fatemi",
"Honglak Lee"
] | null | null | We propose the k-Shortest-Path (k-SP) constraint: a novel constraint on the agent’s trajectory that improves the sample efficiency in sparse-reward MDPs. We show that any optimal policy necessarily satisfies the k-SP constraint. Notably, the k-SP constraint prevents the policy from exploring state-action pairs along th... | [] | null | 890 | 2107.06405 | title_snapshot |
v139/song21a | Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving | https://proceedings.mlr.press/v139/song21a.html | [
"Yang Song",
"Chenlin Meng",
"Renjie Liao",
"Stefano Ermon"
] | null | null | Feedforward computation, such as evaluating a neural network or sampling from an autoregressive model, is ubiquitous in machine learning. The sequential nature of feedforward computation, however, requires a strict order of execution and cannot be easily accelerated with parallel computing. To enable parallelization, w... | [] | null | 891 | 2002.03629 | title_snapshot |
v139/song21b | PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration | https://proceedings.mlr.press/v139/song21b.html | [
"Yuda Song",
"Wen Sun"
] | null | null | Model-based Reinforcement Learning (RL) is a popular learning paradigm due to its potential sample efficiency compared to model-free RL. However, existing empirical model-based RL approaches lack the ability to explore. This work studies a computationally and statistically efficient model-based algorithm for both Kerne... | [] | null | 892 | 2107.07410 | title_snapshot |
v139/song21c | Fast Sketching of Polynomial Kernels of Polynomial Degree | https://proceedings.mlr.press/v139/song21c.html | [
"Zhao Song",
"David Woodruff",
"Zheng Yu",
"Lichen Zhang"
] | null | null | Kernel methods are fundamental in machine learning, and faster algorithms for kernel approximation provide direct speedups for many core tasks in machine learning. The polynomial kernel is especially important as other kernels can often be approximated by the polynomial kernel via a Taylor series expansion. Recent tech... | [] | null | 893 | 2108.09420 | title_snapshot |
v139/song21d | Variance Reduction via Primal-Dual Accelerated Dual Averaging for Nonsmooth Convex Finite-Sums | https://proceedings.mlr.press/v139/song21d.html | [
"Chaobing Song",
"Stephen J Wright",
"Jelena Diakonikolas"
] | null | null | Structured nonsmooth convex finite-sum optimization appears in many machine learning applications, including support vector machines and least absolute deviation. For the primal-dual formulation of this problem, we propose a novel algorithm called \emph{Variance Reduction via Primal-Dual Accelerated Dual Averaging (\vr... | [] | null | 894 | 2102.13643 | title_snapshot |
v139/song21e | Oblivious Sketching-based Central Path Method for Linear Programming | https://proceedings.mlr.press/v139/song21e.html | [
"Zhao Song",
"Zheng Yu"
] | null | null | In this work, we propose a sketching-based central path method for solving linear programmings, whose running time matches the state of the art results [Cohen, Lee, Song STOC 19; Lee, Song, Zhang COLT 19]. Our method opens up the iterations of the central path method and deploys an "iterate and sketch" approach towards... | [] | null | 895 | null | null |
v139/sontakke21a | Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning | https://proceedings.mlr.press/v139/sontakke21a.html | [
"Sumedh A Sontakke",
"Arash Mehrjou",
"Laurent Itti",
"Bernhard Schölkopf"
] | null | null | Humans show an innate ability to learn the regularities of the world through interaction. By performing experiments in our environment, we are able to discern the causal factors of variation and infer how they affect the dynamics of our world. Analogously, here we attempt to equip reinforcement learning agents with the... | [] | null | 896 | 2010.03110 | title_snapshot |
v139/sordoni21a | Decomposed Mutual Information Estimation for Contrastive Representation Learning | https://proceedings.mlr.press/v139/sordoni21a.html | [
"Alessandro Sordoni",
"Nouha Dziri",
"Hannes Schulz",
"Geoff Gordon",
"Philip Bachman",
"Remi Tachet Des Combes"
] | null | null | Recent contrastive representation learning methods rely on estimating mutual information (MI) between multiple views of an underlying context. E.g., we can derive multiple views of a given image by applying data augmentation, or we can split a sequence into views comprising the past and future of some step in the seque... | [] | null | 897 | 2106.13401 | title_snapshot |
v139/stooke21a | Decoupling Representation Learning from Reinforcement Learning | https://proceedings.mlr.press/v139/stooke21a.html | [
"Adam Stooke",
"Kimin Lee",
"Pieter Abbeel",
"Michael Laskin"
] | null | null | In an effort to overcome limitations of reward-driven feature learning in deep reinforcement learning (RL) from images, we propose decoupling representation learning from policy learning. To this end, we introduce a new unsupervised learning (UL) task, called Augmented Temporal Contrast (ATC), which trains a convolutio... | [] | null | 898 | 2009.08319 | title_snapshot |
v139/su21a | K-shot NAS: Learnable Weight-Sharing for NAS with K-shot Supernets | https://proceedings.mlr.press/v139/su21a.html | [
"Xiu Su",
"Shan You",
"Mingkai Zheng",
"Fei Wang",
"Chen Qian",
"Changshui Zhang",
"Chang Xu"
] | null | null | In one-shot weight sharing for NAS, the weights of each operation (at each layer) are supposed to be identical for all architectures (paths) in the supernet. However, this rules out the possibility of adjusting operation weights to cater for different paths, which limits the reliability of the evaluation results. In th... | [] | null | 899 | 2106.06442 | title_snapshot |
v139/sugiyama21a | More Powerful and General Selective Inference for Stepwise Feature Selection using Homotopy Method | https://proceedings.mlr.press/v139/sugiyama21a.html | [
"Kazuya Sugiyama",
"Vo Nguyen Le Duy",
"Ichiro Takeuchi"
] | null | null | Conditional selective inference (SI) has been actively studied as a new statistical inference framework for data-driven hypotheses. The basic idea of conditional SI is to make inferences conditional on the selection event characterized by a set of linear and/or quadratic inequalities. Conditional SI has been mainly stu... | [] | null | 900 | 2012.13545 | title_judge |
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