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v139/hu21d | Generalizable Episodic Memory for Deep Reinforcement Learning | https://proceedings.mlr.press/v139/hu21d.html | [
"Hao Hu",
"Jianing Ye",
"Guangxiang Zhu",
"Zhizhou Ren",
"Chongjie Zhang"
] | null | null | Episodic memory-based methods can rapidly latch onto past successful strategies by a non-parametric memory and improve sample efficiency of traditional reinforcement learning. However, little effort is put into the continuous domain, where a state is never visited twice, and previous episodic methods fail to efficientl... | [] | null | 401 | 2103.06469 | title_snapshot |
v139/hua21a | A Scalable Deterministic Global Optimization Algorithm for Clustering Problems | https://proceedings.mlr.press/v139/hua21a.html | [
"Kaixun Hua",
"Mingfei Shi",
"Yankai Cao"
] | null | null | The minimum sum-of-squares clustering (MSSC) task, which can be treated as a Mixed Integer Second Order Cone Programming (MISOCP) problem, is rarely investigated in the literature through deterministic optimization to find its global optimal value. In this paper, we modelled the MSSC task as a two-stage optimization pr... | [] | null | 402 | null | null |
v139/huang21a | On Recovering from Modeling Errors Using Testing Bayesian Networks | https://proceedings.mlr.press/v139/huang21a.html | [
"Haiying Huang",
"Adnan Darwiche"
] | null | null | We consider the problem of supervised learning with Bayesian Networks when the used dependency structure is incomplete due to missing edges or missing variable states. These modeling errors induce independence constraints on the learned model that may not hold in the true, data-generating distribution. We provide a uni... | [] | null | 403 | null | null |
v139/huang21b | A Novel Sequential Coreset Method for Gradient Descent Algorithms | https://proceedings.mlr.press/v139/huang21b.html | [
"Jiawei Huang",
"Ruomin Huang",
"Wenjie Liu",
"Nikolaos Freris",
"Hu Ding"
] | null | null | A wide range of optimization problems arising in machine learning can be solved by gradient descent algorithms, and a central question in this area is how to efficiently compress a large-scale dataset so as to reduce the computational complexity. Coreset is a popular data compression technique that has been extensively... | [] | null | 404 | 2112.02504 | title_snapshot |
v139/huang21c | FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis | https://proceedings.mlr.press/v139/huang21c.html | [
"Baihe Huang",
"Xiaoxiao Li",
"Zhao Song",
"Xin Yang"
] | null | null | Federated Learning (FL) is an emerging learning scheme that allows different distributed clients to train deep neural networks together without data sharing. Neural networks have become popular due to their unprecedented success. To the best of our knowledge, the theoretical guarantees of FL concerning neural networks ... | [] | null | 405 | 2105.05001 | title_judge |
v139/huang21d | STRODE: Stochastic Boundary Ordinary Differential Equation | https://proceedings.mlr.press/v139/huang21d.html | [
"Hengguan Huang",
"Hongfu Liu",
"Hao Wang",
"Chang Xiao",
"Ye Wang"
] | null | null | Perception of time from sequentially acquired sensory inputs is rooted in everyday behaviors of individual organisms. Yet, most algorithms for time-series modeling fail to learn dynamics of random event timings directly from visual or audio inputs, requiring timing annotations during training that are usually unavailab... | [] | null | 406 | 2107.08273 | title_snapshot |
v139/huang21e | A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distance | https://proceedings.mlr.press/v139/huang21e.html | [
"Minhui Huang",
"Shiqian Ma",
"Lifeng Lai"
] | null | null | The Wasserstein distance has become increasingly important in machine learning and deep learning. Despite its popularity, the Wasserstein distance is hard to approximate because of the curse of dimensionality. A recently proposed approach to alleviate the curse of dimensionality is to project the sampled data from the ... | [] | null | 407 | 2012.05199 | title_snapshot |
v139/huang21f | Projection Robust Wasserstein Barycenters | https://proceedings.mlr.press/v139/huang21f.html | [
"Minhui Huang",
"Shiqian Ma",
"Lifeng Lai"
] | null | null | Collecting and aggregating information from several probability measures or histograms is a fundamental task in machine learning. One of the popular solution methods for this task is to compute the barycenter of the probability measures under the Wasserstein metric. However, approximating the Wasserstein barycenter is ... | [] | null | 408 | 2102.03390 | title_snapshot |
v139/hubara21a | Accurate Post Training Quantization With Small Calibration Sets | https://proceedings.mlr.press/v139/hubara21a.html | [
"Itay Hubara",
"Yury Nahshan",
"Yair Hanani",
"Ron Banner",
"Daniel Soudry"
] | null | null | Lately, post-training quantization methods have gained considerable attention, as they are simple to use, and require only a small unlabeled calibration set. This small dataset cannot be used to fine-tune the model without significant over-fitting. Instead, these methods only use the calibration set to set the activati... | [] | null | 409 | null | null |
v139/hubert21a | Learning and Planning in Complex Action Spaces | https://proceedings.mlr.press/v139/hubert21a.html | [
"Thomas Hubert",
"Julian Schrittwieser",
"Ioannis Antonoglou",
"Mohammadamin Barekatain",
"Simon Schmitt",
"David Silver"
] | null | null | Many important real-world problems have action spaces that are high-dimensional, continuous or both, making full enumeration of all possible actions infeasible. Instead, only small subsets of actions can be sampled for the purpose of policy evaluation and improvement. In this paper, we propose a general framework to re... | [] | null | 410 | 2104.06303 | title_snapshot |
v139/hudson21a | Generative Adversarial Transformers | https://proceedings.mlr.press/v139/hudson21a.html | [
"Drew A Hudson",
"Larry Zitnick"
] | null | null | We introduce the GANsformer, a novel and efficient type of transformer, and explore it for the task of visual generative modeling. The network employs a bipartite structure that enables long-range interactions across the image, while maintaining computation of linear efficiency, that can readily scale to high-resolutio... | [] | null | 411 | 2103.01209 | title_snapshot |
v139/hussain21a | Neural Pharmacodynamic State Space Modeling | https://proceedings.mlr.press/v139/hussain21a.html | [
"Zeshan M Hussain",
"Rahul G. Krishnan",
"David Sontag"
] | null | null | Modeling the time-series of high-dimensional, longitudinal data is important for predicting patient disease progression. However, existing neural network based approaches that learn representations of patient state, while very flexible, are susceptible to overfitting. We propose a deep generative model that makes use o... | [] | null | 412 | 2102.11218 | title_snapshot |
v139/hussenot21a | Hyperparameter Selection for Imitation Learning | https://proceedings.mlr.press/v139/hussenot21a.html | [
"Léonard Hussenot",
"Marcin Andrychowicz",
"Damien Vincent",
"Robert Dadashi",
"Anton Raichuk",
"Sabela Ramos",
"Nikola Momchev",
"Sertan Girgin",
"Raphael Marinier",
"Lukasz Stafiniak",
"Manu Orsini",
"Olivier Bachem",
"Matthieu Geist",
"Olivier Pietquin"
] | null | null | We address the issue of tuning hyperparameters (HPs) for imitation learning algorithms in the context of continuous-control, when the underlying reward function of the demonstrating expert cannot be observed at any time. The vast literature in imitation learning mostly considers this reward function to be available for... | [] | null | 413 | 2105.12034 | title_snapshot |
v139/huster21a | Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions | https://proceedings.mlr.press/v139/huster21a.html | [
"Todd Huster",
"Jeremy Cohen",
"Zinan Lin",
"Kevin Chan",
"Charles Kamhoua",
"Nandi O. Leslie",
"Cho-Yu Jason Chiang",
"Vyas Sekar"
] | null | null | Generative adversarial networks (GANs) are often billed as "universal distribution learners", but precisely what distributions they can represent and learn is still an open question. Heavy-tailed distributions are prevalent in many different domains such as financial risk-assessment, physics, and epidemiology. We obser... | [] | null | 414 | 2101.09113 | title_snapshot |
v139/hutchinson21a | LieTransformer: Equivariant Self-Attention for Lie Groups | https://proceedings.mlr.press/v139/hutchinson21a.html | [
"Michael J Hutchinson",
"Charline Le Lan",
"Sheheryar Zaidi",
"Emilien Dupont",
"Yee Whye Teh",
"Hyunjik Kim"
] | null | null | Group equivariant neural networks are used as building blocks of group invariant neural networks, which have been shown to improve generalisation performance and data efficiency through principled parameter sharing. Such works have mostly focused on group equivariant convolutions, building on the result that group equi... | [] | null | 415 | 2012.10885 | title_snapshot |
v139/ibrahim21a | Crowdsourcing via Annotator Co-occurrence Imputation and Provable Symmetric Nonnegative Matrix Factorization | https://proceedings.mlr.press/v139/ibrahim21a.html | [
"Shahana Ibrahim",
"Xiao Fu"
] | null | null | Unsupervised learning of the Dawid-Skene (D&S) model from noisy, incomplete and crowdsourced annotations has been a long-standing challenge, and is a critical step towards reliably labeling massive data. A recent work takes a coupled nonnegative matrix factorization (CNMF) perspective, and shows appealing features: It ... | [] | null | 416 | 2106.07193 | title_snapshot |
v139/ilse21a | Selecting Data Augmentation for Simulating Interventions | https://proceedings.mlr.press/v139/ilse21a.html | [
"Maximilian Ilse",
"Jakub M Tomczak",
"Patrick Forré"
] | null | null | Machine learning models trained with purely observational data and the principle of empirical risk minimization (Vapnik 1992) can fail to generalize to unseen domains. In this paper, we focus on the case where the problem arises through spurious correlation between the observed domains and the actual task labels. We fi... | [] | null | 417 | 2005.01856 | title_snapshot |
v139/immer21a | Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning | https://proceedings.mlr.press/v139/immer21a.html | [
"Alexander Immer",
"Matthias Bauer",
"Vincent Fortuin",
"Gunnar Rätsch",
"Khan Mohammad Emtiyaz"
] | null | null | Marginal-likelihood based model-selection, even though promising, is rarely used in deep learning due to estimation difficulties. Instead, most approaches rely on validation data, which may not be readily available. In this work, we present a scalable marginal-likelihood estimation method to select both hyperparameters... | [] | null | 418 | 2104.04975 | title_snapshot |
v139/inatsu21a | Active Learning for Distributionally Robust Level-Set Estimation | https://proceedings.mlr.press/v139/inatsu21a.html | [
"Yu Inatsu",
"Shogo Iwazaki",
"Ichiro Takeuchi"
] | null | null | Many cases exist in which a black-box function $f$ with high evaluation cost depends on two types of variables $\bm x$ and $\bm w$, where $\bm x$ is a controllable \emph{design} variable and $\bm w$ are uncontrollable \emph{environmental} variables that have random variation following a certain distribution $P$. In suc... | [] | null | 419 | 2102.04000 | title_snapshot |
v139/indelman21a | Learning Randomly Perturbed Structured Predictors for Direct Loss Minimization | https://proceedings.mlr.press/v139/indelman21a.html | [
"Hedda Cohen Indelman",
"Tamir Hazan"
] | null | null | Direct loss minimization is a popular approach for learning predictors over structured label spaces. This approach is computationally appealing as it replaces integration with optimization and allows to propagate gradients in a deep net using loss-perturbed prediction. Recently, this technique was extended to generativ... | [] | null | 420 | 2007.05724 | title_snapshot |
v139/iqbal21a | Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning | https://proceedings.mlr.press/v139/iqbal21a.html | [
"Shariq Iqbal",
"Christian A Schroeder De Witt",
"Bei Peng",
"Wendelin Boehmer",
"Shimon Whiteson",
"Fei Sha"
] | null | null | Multi-agent settings in the real world often involve tasks with varying types and quantities of agents and non-agent entities; however, common patterns of behavior often emerge among these agents/entities. Our method aims to leverage these commonalities by asking the question: “What is the expected utility of each agen... | [] | null | 421 | 2006.04222 | title_snapshot |
v139/ishfaq21a | Randomized Exploration in Reinforcement Learning with General Value Function Approximation | https://proceedings.mlr.press/v139/ishfaq21a.html | [
"Haque Ishfaq",
"Qiwen Cui",
"Viet Nguyen",
"Alex Ayoub",
"Zhuoran Yang",
"Zhaoran Wang",
"Doina Precup",
"Lin Yang"
] | null | null | We propose a model-free reinforcement learning algorithm inspired by the popular randomized least squares value iteration (RLSVI) algorithm as well as the optimism principle. Unlike existing upper-confidence-bound (UCB) based approaches, which are often computationally intractable, our algorithm drives exploration by s... | [] | null | 422 | 2106.07841 | title_judge |
v139/islamov21a | Distributed Second Order Methods with Fast Rates and Compressed Communication | https://proceedings.mlr.press/v139/islamov21a.html | [
"Rustem Islamov",
"Xun Qian",
"Peter Richtarik"
] | null | null | We develop several new communication-efficient second-order methods for distributed optimization. Our first method, NEWTON-STAR, is a variant of Newton’s method from which it inherits its fast local quadratic rate. However, unlike Newton’s method, NEWTON-STAR enjoys the same per iteration communication cost as gradient... | [] | null | 423 | 2102.07158 | title_snapshot |
v139/izmailov21a | What Are Bayesian Neural Network Posteriors Really Like? | https://proceedings.mlr.press/v139/izmailov21a.html | [
"Pavel Izmailov",
"Sharad Vikram",
"Matthew D Hoffman",
"Andrew Gordon Gordon Wilson"
] | null | null | The posterior over Bayesian neural network (BNN) parameters is extremely high-dimensional and non-convex. For computational reasons, researchers approximate this posterior using inexpensive mini-batch methods such as mean-field variational inference or stochastic-gradient Markov chain Monte Carlo (SGMCMC). To investiga... | [] | null | 424 | 2104.14421 | title_snapshot |
v139/izzo21a | How to Learn when Data Reacts to Your Model: Performative Gradient Descent | https://proceedings.mlr.press/v139/izzo21a.html | [
"Zachary Izzo",
"Lexing Ying",
"James Zou"
] | null | null | Performative distribution shift captures the setting where the choice of which ML model is deployed changes the data distribution. For example, a bank which uses the number of open credit lines to determine a customer’s risk of default on a loan may induce customers to open more credit lines in order to improve their c... | [] | null | 425 | 2102.07698 | title_snapshot |
v139/jaegle21a | Perceiver: General Perception with Iterative Attention | https://proceedings.mlr.press/v139/jaegle21a.html | [
"Andrew Jaegle",
"Felix Gimeno",
"Andy Brock",
"Oriol Vinyals",
"Andrew Zisserman",
"Joao Carreira"
] | null | null | Biological systems understand the world by simultaneously processing high-dimensional inputs from modalities as diverse as vision, audition, touch, proprioception, etc. The perception models used in deep learning on the other hand are designed for individual modalities, often relying on domain-specific assumptions such... | [] | null | 426 | 2103.03206 | title_snapshot |
v139/jaegle21b | Imitation by Predicting Observations | https://proceedings.mlr.press/v139/jaegle21b.html | [
"Andrew Jaegle",
"Yury Sulsky",
"Arun Ahuja",
"Jake Bruce",
"Rob Fergus",
"Greg Wayne"
] | null | null | Imitation learning enables agents to reuse and adapt the hard-won expertise of others, offering a solution to several key challenges in learning behavior. Although it is easy to observe behavior in the real-world, the underlying actions may not be accessible. We present a new method for imitation solely from observatio... | [] | null | 427 | 2107.03851 | title_snapshot |
v139/jafarov21a | Local Correlation Clustering with Asymmetric Classification Errors | https://proceedings.mlr.press/v139/jafarov21a.html | [
"Jafar Jafarov",
"Sanchit Kalhan",
"Konstantin Makarychev",
"Yury Makarychev"
] | null | null | In the Correlation Clustering problem, we are given a complete weighted graph $G$ with its edges labeled as “similar" and “dissimilar" by a noisy binary classifier. For a clustering $\mathcal{C}$ of graph $G$, a similar edge is in disagreement with $\mathcal{C}$, if its endpoints belong to distinct clusters; and a diss... | [] | null | 428 | 2108.05697 | title_snapshot |
v139/jagadeesan21a | Alternative Microfoundations for Strategic Classification | https://proceedings.mlr.press/v139/jagadeesan21a.html | [
"Meena Jagadeesan",
"Celestine Mendler-Dünner",
"Moritz Hardt"
] | null | null | When reasoning about strategic behavior in a machine learning context it is tempting to combine standard microfoundations of rational agents with the statistical decision theory underlying classification. In this work, we argue that a direct combination of these ingredients leads to brittle solution concepts of limited... | [] | null | 429 | 2106.12705 | title_snapshot |
v139/jain21a | Robust Density Estimation from Batches: The Best Things in Life are (Nearly) Free | https://proceedings.mlr.press/v139/jain21a.html | [
"Ayush Jain",
"Alon Orlitsky"
] | null | null | In many applications data are collected in batches, some potentially biased, corrupt, or even adversarial. Learning algorithms for this setting have therefore garnered considerable recent attention. In particular, a sequence of works has shown that all approximately piecewise polynomial distributions—and in particular ... | [] | null | 430 | null | null |
v139/jalal21a | Instance-Optimal Compressed Sensing via Posterior Sampling | https://proceedings.mlr.press/v139/jalal21a.html | [
"Ajil Jalal",
"Sushrut Karmalkar",
"Alex Dimakis",
"Eric Price"
] | null | null | We characterize the measurement complexity of compressed sensing of signals drawn from a known prior distribution, even when the support of the prior is the entire space (rather than, say, sparse vectors). We show for Gaussian measurements and \emph{any} prior distribution on the signal, that the posterior sampling est... | [] | null | 431 | 2106.11438 | title_snapshot |
v139/jalal21b | Fairness for Image Generation with Uncertain Sensitive Attributes | https://proceedings.mlr.press/v139/jalal21b.html | [
"Ajil Jalal",
"Sushrut Karmalkar",
"Jessica Hoffmann",
"Alex Dimakis",
"Eric Price"
] | null | null | This work tackles the issue of fairness in the context of generative procedures, such as image super-resolution, which entail different definitions from the standard classification setting. Moreover, while traditional group fairness definitions are typically defined with respect to specified protected groups – camoufla... | [] | null | 432 | 2106.12182 | title_snapshot |
v139/jalalzai21a | Feature Clustering for Support Identification in Extreme Regions | https://proceedings.mlr.press/v139/jalalzai21a.html | [
"Hamid Jalalzai",
"Rémi Leluc"
] | null | null | Understanding the complex structure of multivariate extremes is a major challenge in various fields from portfolio monitoring and environmental risk management to insurance. In the framework of multivariate Extreme Value Theory, a common characterization of extremes’ dependence structure is the angular measure. It is a... | [] | null | 433 | 2008.07365 | title_snapshot |
v139/jang21a | Improved Regret Bounds of Bilinear Bandits using Action Space Analysis | https://proceedings.mlr.press/v139/jang21a.html | [
"Kyoungseok Jang",
"Kwang-Sung Jun",
"Se-Young Yun",
"Wanmo Kang"
] | null | null | We consider the bilinear bandit problem where the learner chooses a pair of arms, each from two different action spaces of dimension $d_1$ and $d_2$, respectively. The learner then receives a reward whose expectation is a bilinear function of the two chosen arms with an unknown matrix parameter $\Theta^*\in\mathbb{R}^{... | [] | null | 434 | null | null |
v139/jarrett21a | Inverse Decision Modeling: Learning Interpretable Representations of Behavior | https://proceedings.mlr.press/v139/jarrett21a.html | [
"Daniel Jarrett",
"Alihan Hüyük",
"Mihaela Van Der Schaar"
] | null | null | Decision analysis deals with modeling and enhancing decision processes. A principal challenge in improving behavior is in obtaining a transparent *description* of existing behavior in the first place. In this paper, we develop an expressive, unifying perspective on *inverse decision modeling*: a framework for learning ... | [] | null | 435 | 2310.18591 | title_snapshot |
v139/jastrzebski21a | Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization | https://proceedings.mlr.press/v139/jastrzebski21a.html | [
"Stanislaw Jastrzebski",
"Devansh Arpit",
"Oliver Astrand",
"Giancarlo B Kerg",
"Huan Wang",
"Caiming Xiong",
"Richard Socher",
"Kyunghyun Cho",
"Krzysztof J Geras"
] | null | null | The early phase of training a deep neural network has a dramatic effect on the local curvature of the loss function. For instance, using a small learning rate does not guarantee stable optimization because the optimization trajectory has a tendency to steer towards regions of the loss surface with increasing local curv... | [] | null | 436 | 2012.14193 | title_snapshot |
v139/javed21a | Policy Gradient Bayesian Robust Optimization for Imitation Learning | https://proceedings.mlr.press/v139/javed21a.html | [
"Zaynah Javed",
"Daniel S Brown",
"Satvik Sharma",
"Jerry Zhu",
"Ashwin Balakrishna",
"Marek Petrik",
"Anca Dragan",
"Ken Goldberg"
] | null | null | The difficulty in specifying rewards for many real-world problems has led to an increased focus on learning rewards from human feedback, such as demonstrations. However, there are often many different reward functions that explain the human feedback, leaving agents with uncertainty over what the true reward function is... | [] | null | 437 | 2106.06499 | title_snapshot |
v139/jayaram21a | In-Database Regression in Input Sparsity Time | https://proceedings.mlr.press/v139/jayaram21a.html | [
"Rajesh Jayaram",
"Alireza Samadian",
"David Woodruff",
"Peng Ye"
] | null | null | Sketching is a powerful dimensionality reduction technique for accelerating algorithms for data analysis. A crucial step in sketching methods is to compute a subspace embedding (SE) for a large matrix $A \in \mathbb{R}^{N \times d}$. SE’s are the primary tool for obtaining extremely efficient solutions for many linear-... | [] | null | 438 | 2107.05672 | title_snapshot |
v139/jayaram21b | Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics | https://proceedings.mlr.press/v139/jayaram21b.html | [
"Vivek Jayaram",
"John Thickstun"
] | null | null | This paper introduces an alternative approach to sampling from autoregressive models. Autoregressive models are typically sampled sequentially, according to the transition dynamics defined by the model. Instead, we propose a sampling procedure that initializes a sequence with white noise and follows a Markov chain defi... | [] | null | 439 | 2105.08164 | title_snapshot |
v139/jeong21a | Objective Bound Conditional Gaussian Process for Bayesian Optimization | https://proceedings.mlr.press/v139/jeong21a.html | [
"Taewon Jeong",
"Heeyoung Kim"
] | null | null | A Gaussian process is a standard surrogate model for an unknown objective function in Bayesian optimization. In this paper, we propose a new surrogate model, called the objective bound conditional Gaussian process (OBCGP), to condition a Gaussian process on a bound on the optimal function value. The bound is obtained a... | [] | null | 440 | null | null |
v139/jesson21a | Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding | https://proceedings.mlr.press/v139/jesson21a.html | [
"Andrew Jesson",
"Sören Mindermann",
"Yarin Gal",
"Uri Shalit"
] | null | null | We study the problem of learning conditional average treatment effects (CATE) from high-dimensional, observational data with unobserved confounders. Unobserved confounders introduce ignorance—a level of unidentifiability—about an individual’s response to treatment by inducing bias in CATE estimates. We present a new pa... | [] | null | 441 | 2103.04850 | title_snapshot |
v139/jha21a | DeepReDuce: ReLU Reduction for Fast Private Inference | https://proceedings.mlr.press/v139/jha21a.html | [
"Nandan Kumar Jha",
"Zahra Ghodsi",
"Siddharth Garg",
"Brandon Reagen"
] | null | null | The recent rise of privacy concerns has led researchers to devise methods for private neural inference—where inferences are made directly on encrypted data, never seeing inputs. The primary challenge facing private inference is that computing on encrypted data levies an impractically-high latency penalty, stemming most... | [] | null | 442 | 2103.01396 | title_snapshot |
v139/jha21b | Factor-analytic inverse regression for high-dimension, small-sample dimensionality reduction | https://proceedings.mlr.press/v139/jha21b.html | [
"Aditi Jha",
"Michael J. Morais",
"Jonathan W Pillow"
] | null | null | Sufficient dimension reduction (SDR) methods are a family of supervised methods for dimensionality reduction that seek to reduce dimensionality while preserving information about a target variable of interest. However, existing SDR methods typically require more observations than the number of dimensions ($N > p$). To ... | [] | null | 443 | null | null |
v139/ji21a | Fast margin maximization via dual acceleration | https://proceedings.mlr.press/v139/ji21a.html | [
"Ziwei Ji",
"Nathan Srebro",
"Matus Telgarsky"
] | null | null | We present and analyze a momentum-based gradient method for training linear classifiers with an exponentially-tailed loss (e.g., the exponential or logistic loss), which maximizes the classification margin on separable data at a rate of O(1/t^2). This contrasts with a rate of O(1/log(t)) for standard gradient descent, ... | [] | null | 444 | 2107.00595 | title_snapshot |
v139/ji21b | Marginalized Stochastic Natural Gradients for Black-Box Variational Inference | https://proceedings.mlr.press/v139/ji21b.html | [
"Geng Ji",
"Debora Sujono",
"Erik B Sudderth"
] | null | null | Black-box variational inference algorithms use stochastic sampling to analyze diverse statistical models, like those expressed in probabilistic programming languages, without model-specific derivations. While the popular score-function estimator computes unbiased gradient estimates, its variance is often unacceptably l... | [] | null | 445 | null | null |
v139/ji21c | Bilevel Optimization: Convergence Analysis and Enhanced Design | https://proceedings.mlr.press/v139/ji21c.html | [
"Kaiyi Ji",
"Junjie Yang",
"Yingbin Liang"
] | null | null | Bilevel optimization has arisen as a powerful tool for many machine learning problems such as meta-learning, hyperparameter optimization, and reinforcement learning. In this paper, we investigate the nonconvex-strongly-convex bilevel optimization problem. For deterministic bilevel optimization, we provide a comprehensi... | [] | null | 446 | 2010.07962 | title_snapshot |
v139/jia21a | Efficient Statistical Tests: A Neural Tangent Kernel Approach | https://proceedings.mlr.press/v139/jia21a.html | [
"Sheng Jia",
"Ehsan Nezhadarya",
"Yuhuai Wu",
"Jimmy Ba"
] | null | null | For machine learning models to make reliable predictions in deployment, one needs to ensure the previously unknown test samples need to be sufficiently similar to the training data. The commonly used shift-invariant kernels do not have the compositionality and fail to capture invariances in high-dimensional data in com... | [] | null | 447 | null | null |
v139/jia21b | Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision | https://proceedings.mlr.press/v139/jia21b.html | [
"Chao Jia",
"Yinfei Yang",
"Ye Xia",
"Yi-Ting Chen",
"Zarana Parekh",
"Hieu Pham",
"Quoc Le",
"Yun-Hsuan Sung",
"Zhen Li",
"Tom Duerig"
] | null | null | Pre-trained representations are becoming crucial for many NLP and perception tasks. While representation learning in NLP has transitioned to training on raw text without human annotations, visual and vision-language representations still rely heavily on curated training datasets that are expensive or require expert kno... | [] | null | 448 | 2102.05918 | title_snapshot |
v139/jia21c | Multi-Dimensional Classification via Sparse Label Encoding | https://proceedings.mlr.press/v139/jia21c.html | [
"Bin-Bin Jia",
"Min-Ling Zhang"
] | null | null | In multi-dimensional classification (MDC), there are multiple class variables in the output space with each of them corresponding to one heterogeneous class space. Due to the heterogeneity of class spaces, it is quite challenging to consider the dependencies among class variables when learning from MDC examples. In thi... | [] | null | 449 | null | null |
v139/jiang21a | Self-Damaging Contrastive Learning | https://proceedings.mlr.press/v139/jiang21a.html | [
"Ziyu Jiang",
"Tianlong Chen",
"Bobak J Mortazavi",
"Zhangyang Wang"
] | null | null | The recent breakthrough achieved by contrastive learning accelerates the pace for deploying unsupervised training on real-world data applications. However, unlabeled data in reality is commonly imbalanced and shows a long-tail distribution, and it is unclear how robustly the latest contrastive learning methods could pe... | [] | null | 450 | 2106.02990 | title_snapshot |
v139/jiang21b | Prioritized Level Replay | https://proceedings.mlr.press/v139/jiang21b.html | [
"Minqi Jiang",
"Edward Grefenstette",
"Tim Rocktäschel"
] | null | null | Environments with procedurally generated content serve as important benchmarks for testing systematic generalization in deep reinforcement learning. In this setting, each level is an algorithmically created environment instance with a unique configuration of its factors of variation. Training on a prespecified subset o... | [] | null | 451 | 2010.03934 | title_snapshot |
v139/jiang21c | Monotonic Robust Policy Optimization with Model Discrepancy | https://proceedings.mlr.press/v139/jiang21c.html | [
"Yuankun Jiang",
"Chenglin Li",
"Wenrui Dai",
"Junni Zou",
"Hongkai Xiong"
] | null | null | State-of-the-art deep reinforcement learning (DRL) algorithms tend to overfit due to the model discrepancy between source and target environments. Though applying domain randomization during training can improve the average performance by randomly generating a sufficient diversity of environments in simulator, the wors... | [] | null | 452 | null | null |
v139/jiang21d | Approximation Theory of Convolutional Architectures for Time Series Modelling | https://proceedings.mlr.press/v139/jiang21d.html | [
"Haotian Jiang",
"Zhong Li",
"Qianxiao Li"
] | null | null | We study the approximation properties of convolutional architectures applied to time series modelling, which can be formulated mathematically as a functional approximation problem. In the recurrent setting, recent results reveal an intricate connection between approximation efficiency and memory structures in the data ... | [] | null | 453 | 2107.09355 | title_snapshot |
v139/jiang21e | Streaming and Distributed Algorithms for Robust Column Subset Selection | https://proceedings.mlr.press/v139/jiang21e.html | [
"Shuli Jiang",
"Dennis Li",
"Irene Mengze Li",
"Arvind V Mahankali",
"David Woodruff"
] | null | null | We give the first single-pass streaming algorithm for Column Subset Selection with respect to the entrywise $\ell_p$-norm with $1 \leq p < 2$. We study the $\ell_p$ norm loss since it is often considered more robust to noise than the standard Frobenius norm. Given an input matrix $A \in \mathbb{R}^{d \times n}$ ($n \gg... | [] | null | 454 | 2107.07657 | title_snapshot |
v139/jiang21f | Single Pass Entrywise-Transformed Low Rank Approximation | https://proceedings.mlr.press/v139/jiang21f.html | [
"Yifei Jiang",
"Yi Li",
"Yiming Sun",
"Jiaxin Wang",
"David Woodruff"
] | null | null | In applications such as natural language processing or computer vision, one is given a large $n \times n$ matrix $A = (a_{i,j})$ and would like to compute a matrix decomposition, e.g., a low rank approximation, of a function $f(A) = (f(a_{i,j}))$ applied entrywise to $A$. A very important special case is the likelihood... | [] | null | 455 | 2107.07889 | title_snapshot |
v139/jiang21g | The Emergence of Individuality | https://proceedings.mlr.press/v139/jiang21g.html | [
"Jiechuan Jiang",
"Zongqing Lu"
] | null | null | Individuality is essential in human society. It induces the division of labor and thus improves the efficiency and productivity. Similarly, it should also be a key to multi-agent cooperation. Inspired by that individuality is of being an individual separate from others, we propose a simple yet efficient method for the ... | [] | null | 456 | 2006.05842 | title_snapshot |
v139/jiang21h | Online Selection Problems against Constrained Adversary | https://proceedings.mlr.press/v139/jiang21h.html | [
"Zhihao Jiang",
"Pinyan Lu",
"Zhihao Gavin Tang",
"Yuhao Zhang"
] | null | null | Inspired by a recent line of work in online algorithms with predictions, we study the constrained adversary model that utilizes predictions from a different perspective. Prior works mostly focused on designing simultaneously robust and consistent algorithms, without making assumptions on the quality of the predictions.... | [] | null | 457 | null | null |
v139/jiang21i | Active Covering | https://proceedings.mlr.press/v139/jiang21i.html | [
"Heinrich Jiang",
"Afshin Rostamizadeh"
] | null | null | We analyze the problem of active covering, where the learner is given an unlabeled dataset and can sequentially label query examples. The objective is to label query all of the positive examples in the fewest number of total label queries. We show under standard non-parametric assumptions that a classical support estim... | [] | null | 458 | 2106.02552 | title_snapshot |
v139/jiang21j | Emphatic Algorithms for Deep Reinforcement Learning | https://proceedings.mlr.press/v139/jiang21j.html | [
"Ray Jiang",
"Tom Zahavy",
"Zhongwen Xu",
"Adam White",
"Matteo Hessel",
"Charles Blundell",
"Hado Van Hasselt"
] | null | null | Off-policy learning allows us to learn about possible policies of behavior from experience generated by a different behavior policy. Temporal difference (TD) learning algorithms can become unstable when combined with function approximation and off-policy sampling—this is known as the “deadly triad”. Emphatic temporal d... | [] | null | 459 | 2106.11779 | title_snapshot |
v139/jiang21k | Characterizing Structural Regularities of Labeled Data in Overparameterized Models | https://proceedings.mlr.press/v139/jiang21k.html | [
"Ziheng Jiang",
"Chiyuan Zhang",
"Kunal Talwar",
"Michael C Mozer"
] | null | null | Humans are accustomed to environments that contain both regularities and exceptions. For example, at most gas stations, one pays prior to pumping, but the occasional rural station does not accept payment in advance. Likewise, deep neural networks can generalize across instances that share common patterns or structures,... | [] | null | 460 | 2002.03206 | title_snapshot |
v139/jin21a | Optimal Streaming Algorithms for Multi-Armed Bandits | https://proceedings.mlr.press/v139/jin21a.html | [
"Tianyuan Jin",
"Keke Huang",
"Jing Tang",
"Xiaokui Xiao"
] | null | null | This paper studies two variants of the best arm identification (BAI) problem under the streaming model, where we have a stream of n arms with reward distributions supported on [0,1] with unknown means. The arms in the stream are arriving one by one, and the algorithm cannot access an arm unless it is stored in a limite... | [] | null | 461 | 2410.17835 | title_snapshot |
v139/jin21b | Towards Tight Bounds on the Sample Complexity of Average-reward MDPs | https://proceedings.mlr.press/v139/jin21b.html | [
"Yujia Jin",
"Aaron Sidford"
] | null | null | We prove new upper and lower bounds for sample complexity of finding an $\epsilon$-optimal policy of an infinite-horizon average-reward Markov decision process (MDP) given access to a generative model. When the mixing time of the probability transition matrix of all policies is at most $t_\mathrm{mix}$, we provide an a... | [] | null | 462 | 2106.07046 | title_snapshot |
v139/jin21c | Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits | https://proceedings.mlr.press/v139/jin21c.html | [
"Tianyuan Jin",
"Jing Tang",
"Pan Xu",
"Keke Huang",
"Xiaokui Xiao",
"Quanquan Gu"
] | null | null | In batched multi-armed bandit problems, the learner can adaptively pull arms and adjust strategy in batches. In many real applications, not only the regret but also the batch complexity need to be optimized. Existing batched bandit algorithms usually assume that the time horizon T is known in advance. However, many app... | [] | null | 463 | null | null |
v139/jin21d | MOTS: Minimax Optimal Thompson Sampling | https://proceedings.mlr.press/v139/jin21d.html | [
"Tianyuan Jin",
"Pan Xu",
"Jieming Shi",
"Xiaokui Xiao",
"Quanquan Gu"
] | null | null | Thompson sampling is one of the most widely used algorithms in many online decision problems due to its simplicity for implementation and superior empirical performance over other state-of-the-art methods. Despite its popularity and empirical success, it has remained an open problem whether Thompson sampling can achiev... | [] | null | 464 | 2003.01803 | title_snapshot |
v139/jin21e | Is Pessimism Provably Efficient for Offline RL? | https://proceedings.mlr.press/v139/jin21e.html | [
"Ying Jin",
"Zhuoran Yang",
"Zhaoran Wang"
] | null | null | We study offline reinforcement learning (RL), which aims to learn an optimal policy based on a dataset collected a priori. Due to the lack of further interactions with the environment, offline RL suffers from the insufficient coverage of the dataset, which eludes most existing theoretical analysis. In this paper, we pr... | [] | null | 465 | 2012.15085 | title_snapshot |
v139/jing21a | Adversarial Option-Aware Hierarchical Imitation Learning | https://proceedings.mlr.press/v139/jing21a.html | [
"Mingxuan Jing",
"Wenbing Huang",
"Fuchun Sun",
"Xiaojian Ma",
"Tao Kong",
"Chuang Gan",
"Lei Li"
] | null | null | It has been a challenge to learning skills for an agent from long-horizon unannotated demonstrations. Existing approaches like Hierarchical Imitation Learning(HIL) are prone to compounding errors or suboptimal solutions. In this paper, we propose Option-GAIL, a novel method to learn skills at long horizon. The key idea... | [] | null | 466 | 2106.05530 | title_snapshot |
v139/jo21a | Discrete-Valued Latent Preference Matrix Estimation with Graph Side Information | https://proceedings.mlr.press/v139/jo21a.html | [
"Changhun Jo",
"Kangwook Lee"
] | null | null | Incorporating graph side information into recommender systems has been widely used to better predict ratings, but relatively few works have focused on theoretical guarantees. Ahn et al. (2018) firstly characterized the optimal sample complexity in the presence of graph side information, but the results are limited due ... | [] | null | 467 | 2003.07040 | title_snapshot |
v139/jordan21a | Provable Lipschitz Certification for Generative Models | https://proceedings.mlr.press/v139/jordan21a.html | [
"Matt Jordan",
"Alex Dimakis"
] | null | null | We present a scalable technique for upper bounding the Lipschitz constant of generative models. We relate this quantity to the maximal norm over the set of attainable vector-Jacobian products of a given generative model. We approximate this set by layerwise convex approximations using zonotopes. Our approach generalize... | [] | null | 468 | 2107.02732 | title_snapshot |
v139/jorgensen21a | Isometric Gaussian Process Latent Variable Model for Dissimilarity Data | https://proceedings.mlr.press/v139/jorgensen21a.html | [
"Martin Jørgensen",
"Soren Hauberg"
] | null | null | We present a probabilistic model where the latent variable respects both the distances and the topology of the modeled data. The model leverages the Riemannian geometry of the generated manifold to endow the latent space with a well-defined stochastic distance measure, which is modeled locally as Nakagami distributions... | [] | null | 469 | 2006.11741 | title_snapshot |
v139/ju21a | On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models | https://proceedings.mlr.press/v139/ju21a.html | [
"Peizhong Ju",
"Xiaojun Lin",
"Ness Shroff"
] | null | null | In this paper, we study the generalization performance of min $\ell_2$-norm overfitting solutions for the neural tangent kernel (NTK) model of a two-layer neural network with ReLU activation that has no bias term. We show that, depending on the ground-truth function, the test error of overfitted NTK models exhibits cha... | [] | null | 470 | 2103.05243 | title_snapshot |
v139/jun21a | Improved Confidence Bounds for the Linear Logistic Model and Applications to Bandits | https://proceedings.mlr.press/v139/jun21a.html | [
"Kwang-Sung Jun",
"Lalit Jain",
"Blake Mason",
"Houssam Nassif"
] | null | null | We propose improved fixed-design confidence bounds for the linear logistic model. Our bounds significantly improve upon the state-of-the-art bound by Li et al. (2017) via recent developments of the self-concordant analysis of the logistic loss (Faury et al., 2020). Specifically, our confidence bound avoids a direct dep... | [] | null | 471 | 2011.11222 | title_judge |
v139/jung21a | Detection of Signal in the Spiked Rectangular Models | https://proceedings.mlr.press/v139/jung21a.html | [
"Ji Hyung Jung",
"Hye Won Chung",
"Ji Oon Lee"
] | null | null | We consider the problem of detecting signals in the rank-one signal-plus-noise data matrix models that generalize the spiked Wishart matrices. We show that the principal component analysis can be improved by pre-transforming the matrix entries if the noise is non-Gaussian. As an intermediate step, we prove a sharp phas... | [] | null | 472 | 2104.13517 | title_snapshot |
v139/jung21b | Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning | https://proceedings.mlr.press/v139/jung21b.html | [
"Yonghan Jung",
"Jin Tian",
"Elias Bareinboim"
] | null | null | General methods have been developed for estimating causal effects from observational data under causal assumptions encoded in the form of a causal graph. Most of this literature assumes that the underlying causal graph is completely specified. However, only observational data is available in most practical settings, wh... | [] | null | 473 | null | null |
v139/kaba21a | A Nullspace Property for Subspace-Preserving Recovery | https://proceedings.mlr.press/v139/kaba21a.html | [
"Mustafa D Kaba",
"Chong You",
"Daniel P Robinson",
"Enrique Mallada",
"Rene Vidal"
] | null | null | Much of the theory for classical sparse recovery is based on conditions on the dictionary that are both necessary and sufficient (e.g., nullspace property) or only sufficient (e.g., incoherence and restricted isometry). In contrast, much of the theory for subspace-preserving recovery, the theoretical underpinnings for ... | [] | null | 474 | null | null |
v139/kag21a | Training Recurrent Neural Networks via Forward Propagation Through Time | https://proceedings.mlr.press/v139/kag21a.html | [
"Anil Kag",
"Venkatesh Saligrama"
] | null | null | Back-propagation through time (BPTT) has been widely used for training Recurrent Neural Networks (RNNs). BPTT updates RNN parameters on an instance by back-propagating the error in time over the entire sequence length, and as a result, leads to poor trainability due to the well-known gradient explosion/decay phenomena.... | [] | null | 475 | null | null |
v139/kairouz21a | The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation | https://proceedings.mlr.press/v139/kairouz21a.html | [
"Peter Kairouz",
"Ziyu Liu",
"Thomas Steinke"
] | null | null | We consider training models on private data that are distributed across user devices. To ensure privacy, we add on-device noise and use secure aggregation so that only the noisy sum is revealed to the server. We present a comprehensive end-to-end system, which appropriately discretizes the data and adds discrete Gaussi... | [] | null | 476 | 2102.06387 | title_snapshot |
v139/kairouz21b | Practical and Private (Deep) Learning Without Sampling or Shuffling | https://proceedings.mlr.press/v139/kairouz21b.html | [
"Peter Kairouz",
"Brendan Mcmahan",
"Shuang Song",
"Om Thakkar",
"Abhradeep Thakurta",
"Zheng Xu"
] | null | null | We consider training models with differential privacy (DP) using mini-batch gradients. The existing state-of-the-art, Differentially Private Stochastic Gradient Descent (DP-SGD), requires \emph{privacy amplification by sampling or shuffling} to obtain the best privacy/accuracy/computation trade-offs. Unfortunately, the... | [] | null | 477 | 2103.00039 | title_snapshot |
v139/kajino21a | A Differentiable Point Process with Its Application to Spiking Neural Networks | https://proceedings.mlr.press/v139/kajino21a.html | [
"Hiroshi Kajino"
] | null | null | This paper is concerned about a learning algorithm for a probabilistic model of spiking neural networks (SNNs). Jimenez Rezende & Gerstner (2014) proposed a stochastic variational inference algorithm to train SNNs with hidden neurons. The algorithm updates the variational distribution using the score function gradient ... | [] | null | 478 | 2106.00901 | title_snapshot |
v139/kalantzis21a | Projection techniques to update the truncated SVD of evolving matrices with applications | https://proceedings.mlr.press/v139/kalantzis21a.html | [
"Vasileios Kalantzis",
"Georgios Kollias",
"Shashanka Ubaru",
"Athanasios N. Nikolakopoulos",
"Lior Horesh",
"Kenneth Clarkson"
] | null | null | This submission considers the problem of updating the rank-$k$ truncated Singular Value Decomposition (SVD) of matrices subject to the addition of new rows and/or columns over time. Such matrix problems represent an important computational kernel in applications such as Latent Semantic Indexing and Recommender Systems.... | [] | null | 479 | 2010.06392 | title_judge |
v139/kallus21a | Optimal Off-Policy Evaluation from Multiple Logging Policies | https://proceedings.mlr.press/v139/kallus21a.html | [
"Nathan Kallus",
"Yuta Saito",
"Masatoshi Uehara"
] | null | null | We study off-policy evaluation (OPE) from multiple logging policies, each generating a dataset of fixed size, i.e., stratified sampling. Previous work noted that in this setting the ordering of the variances of different importance sampling estimators is instance-dependent, which brings up a dilemma as to which importa... | [] | null | 480 | 2010.11002 | title_snapshot |
v139/kamoutsi21a | Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations | https://proceedings.mlr.press/v139/kamoutsi21a.html | [
"Angeliki Kamoutsi",
"Goran Banjac",
"John Lygeros"
] | null | null | We consider large-scale Markov decision processes with an unknown cost function and address the problem of learning a policy from a finite set of expert demonstrations. We assume that the learner is not allowed to interact with the expert and has no access to reinforcement signal of any kind. Existing inverse rei... | [] | null | 481 | 2112.14004 | title_snapshot |
v139/kandiros21a | Statistical Estimation from Dependent Data | https://proceedings.mlr.press/v139/kandiros21a.html | [
"Vardis Kandiros",
"Yuval Dagan",
"Nishanth Dikkala",
"Surbhi Goel",
"Constantinos Daskalakis"
] | null | null | We consider a general statistical estimation problem wherein binary labels across different observations are not independent conditioning on their feature vectors, but dependent, capturing settings where e.g. these observations are collected on a spatial domain, a temporal domain, or a social network, which induce depe... | [] | null | 482 | 2107.09773 | title_snapshot |
v139/kapoor21a | SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes | https://proceedings.mlr.press/v139/kapoor21a.html | [
"Sanyam Kapoor",
"Marc Finzi",
"Ke Alexander Wang",
"Andrew Gordon Gordon Wilson"
] | null | null | State-of-the-art methods for scalable Gaussian processes use iterative algorithms, requiring fast matrix vector multiplies (MVMs) with the co-variance kernel. The Structured Kernel Interpolation (SKI) framework accelerates these MVMs by performing efficient MVMs on a grid and interpolating back to the original space. I... | [] | null | 483 | 2106.06695 | title_snapshot |
v139/kapoor21b | Variational Auto-Regressive Gaussian Processes for Continual Learning | https://proceedings.mlr.press/v139/kapoor21b.html | [
"Sanyam Kapoor",
"Theofanis Karaletsos",
"Thang D Bui"
] | null | null | Through sequential construction of posteriors on observing data online, Bayes’ theorem provides a natural framework for continual learning. We develop Variational Auto-Regressive Gaussian Processes (VAR-GPs), a principled posterior updating mechanism to solve sequential tasks in continual learning. By relying on sparse... | [] | null | 484 | 2006.05468 | title_snapshot |
v139/karampatziakis21a | Off-Policy Confidence Sequences | https://proceedings.mlr.press/v139/karampatziakis21a.html | [
"Nikos Karampatziakis",
"Paul Mineiro",
"Aaditya Ramdas"
] | null | null | We develop confidence bounds that hold uniformly over time for off-policy evaluation in the contextual bandit setting. These confidence sequences are based on recent ideas from martingale analysis and are non-asymptotic, non-parametric, and valid at arbitrary stopping times. We provide algorithms for computing these co... | [] | null | 485 | 2102.09540 | title_snapshot |
v139/karimireddy21a | Learning from History for Byzantine Robust Optimization | https://proceedings.mlr.press/v139/karimireddy21a.html | [
"Sai Praneeth Karimireddy",
"Lie He",
"Martin Jaggi"
] | null | null | Byzantine robustness has received significant attention recently given its importance for distributed and federated learning. In spite of this, we identify severe flaws in existing algorithms even when the data across the participants is identically distributed. First, we show realistic examples where current state of ... | [] | null | 486 | 2012.10333 | title_snapshot |
v139/kato21a | Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation | https://proceedings.mlr.press/v139/kato21a.html | [
"Masahiro Kato",
"Takeshi Teshima"
] | null | null | Density ratio estimation (DRE) is at the core of various machine learning tasks such as anomaly detection and domain adaptation. In the DRE literature, existing studies have extensively studied methods based on Bregman divergence (BD) minimization. However, when we apply the BD minimization with highly flexible models,... | [] | null | 487 | 2006.06979 | title_snapshot |
v139/katz-samuels21a | Improved Algorithms for Agnostic Pool-based Active Classification | https://proceedings.mlr.press/v139/katz-samuels21a.html | [
"Julian Katz-Samuels",
"Jifan Zhang",
"Lalit Jain",
"Kevin Jamieson"
] | null | null | We consider active learning for binary classification in the agnostic pool-based setting. The vast majority of works in active learning in the agnostic setting are inspired by the CAL algorithm where each query is uniformly sampled from the disagreement region of the current version space. The sample complexity of such... | [] | null | 488 | 2105.06499 | title_snapshot |
v139/kaya21a | When Does Data Augmentation Help With Membership Inference Attacks? | https://proceedings.mlr.press/v139/kaya21a.html | [
"Yigitcan Kaya",
"Tudor Dumitras"
] | null | null | Deep learning models often raise privacy concerns as they leak information about their training data. This leakage enables membership inference attacks (MIA) that can identify whether a data point was in a model’s training set. Research shows that some ’data augmentation’ mechanisms may reduce the risk by combatting a ... | [] | null | 489 | null | null |
v139/kazemi21a | Regularized Submodular Maximization at Scale | https://proceedings.mlr.press/v139/kazemi21a.html | [
"Ehsan Kazemi",
"Shervin Minaee",
"Moran Feldman",
"Amin Karbasi"
] | null | null | In this paper, we propose scalable methods for maximizing a regularized submodular function $f \triangleq g-\ell$ expressed as the difference between a monotone submodular function $g$ and a modular function $\ell$. Submodularity is inherently related to the notions of diversity, coverage, and representativeness. In pa... | [] | null | 490 | 2002.03503 | title_snapshot |
v139/kelkar21a | Prior Image-Constrained Reconstruction using Style-Based Generative Models | https://proceedings.mlr.press/v139/kelkar21a.html | [
"Varun A Kelkar",
"Mark Anastasio"
] | null | null | Obtaining a useful estimate of an object from highly incomplete imaging measurements remains a holy grail of imaging science. Deep learning methods have shown promise in learning object priors or constraints to improve the conditioning of an ill-posed imaging inverse problem. In this study, a framework for estimating a... | [] | null | 491 | 2102.12525 | title_snapshot |
v139/keller21a | Self Normalizing Flows | https://proceedings.mlr.press/v139/keller21a.html | [
"Thomas A Keller",
"Jorn W.T. Peters",
"Priyank Jaini",
"Emiel Hoogeboom",
"Patrick Forré",
"Max Welling"
] | null | null | Efficient gradient computation of the Jacobian determinant term is a core problem in many machine learning settings, and especially so in the normalizing flow framework. Most proposed flow models therefore either restrict to a function class with easy evaluation of the Jacobian determinant, or an efficient estimator th... | [] | null | 492 | 2011.07248 | title_snapshot |
v139/kenlay21a | Interpretable Stability Bounds for Spectral Graph Filters | https://proceedings.mlr.press/v139/kenlay21a.html | [
"Henry Kenlay",
"Dorina Thanou",
"Xiaowen Dong"
] | null | null | Graph-structured data arise in a variety of real-world context ranging from sensor and transportation to biological and social networks. As a ubiquitous tool to process graph-structured data, spectral graph filters have been used to solve common tasks such as denoising and anomaly detection, as well as design deep lear... | [] | null | 493 | 2102.09587 | title_snapshot |
v139/kerdreux21a | Affine Invariant Analysis of Frank-Wolfe on Strongly Convex Sets | https://proceedings.mlr.press/v139/kerdreux21a.html | [
"Thomas Kerdreux",
"Lewis Liu",
"Simon Lacoste-Julien",
"Damien Scieur"
] | null | null | It is known that the Frank-Wolfe (FW) algorithm, which is affine covariant, enjoys faster convergence rates than $\mathcal{O}\left(1/K\right)$ when the constraint set is strongly convex. However, these results rely on norm-dependent assumptions, usually incurring non-affine invariant bounds, in contradiction with FW’s ... | [] | null | 494 | 2011.03351 | title_snapshot |
v139/khachaturov21a | Markpainting: Adversarial Machine Learning meets Inpainting | https://proceedings.mlr.press/v139/khachaturov21a.html | [
"David Khachaturov",
"Ilia Shumailov",
"Yiren Zhao",
"Nicolas Papernot",
"Ross Anderson"
] | null | null | Inpainting is a learned interpolation technique that is based on generative modeling and used to populate masked or missing pieces in an image; it has wide applications in picture editing and retouching. Recently, inpainting started being used for watermark removal, raising concerns. In this paper we study how to manip... | [] | null | 495 | 2106.00660 | title_snapshot |
v139/khodadadian21a | Finite-Sample Analysis of Off-Policy Natural Actor-Critic Algorithm | https://proceedings.mlr.press/v139/khodadadian21a.html | [
"Sajad Khodadadian",
"Zaiwei Chen",
"Siva Theja Maguluri"
] | null | null | In this paper, we provide finite-sample convergence guarantees for an off-policy variant of the natural actor-critic (NAC) algorithm based on Importance Sampling. In particular, we show that the algorithm converges to a global optimal policy with a sample complexity of $\mathcal{O}(\epsilon^{-3}\log^2(1/\epsilon))$ und... | [] | null | 496 | 2102.09318 | title_snapshot |
v139/khrulkov21a | Functional Space Analysis of Local GAN Convergence | https://proceedings.mlr.press/v139/khrulkov21a.html | [
"Valentin Khrulkov",
"Artem Babenko",
"Ivan Oseledets"
] | null | null | Recent work demonstrated the benefits of studying continuous-time dynamics governing the GAN training. However, this dynamics is analyzed in the model parameter space, which results in finite-dimensional dynamical systems. We propose a novel perspective where we study the local dynamics of adversarial training in the g... | [] | null | 497 | 2102.04448 | title_snapshot |
v139/kidger21a | "Hey, that’s not an ODE": Faster ODE Adjoints via Seminorms | https://proceedings.mlr.press/v139/kidger21a.html | [
"Patrick Kidger",
"Ricky T. Q. Chen",
"Terry J Lyons"
] | null | null | Neural differential equations may be trained by backpropagating gradients via the adjoint method, which is another differential equation typically solved using an adaptive-step-size numerical differential equation solver. A proposed step is accepted if its error, \emph{relative to some norm}, is sufficiently small; els... | [] | null | 498 | 2009.09457 | title_snapshot |
v139/kidger21b | Neural SDEs as Infinite-Dimensional GANs | https://proceedings.mlr.press/v139/kidger21b.html | [
"Patrick Kidger",
"James Foster",
"Xuechen Li",
"Terry J Lyons"
] | null | null | Stochastic differential equations (SDEs) are a staple of mathematical modelling of temporal dynamics. However, a fundamental limitation has been that such models have typically been relatively inflexible, which recent work introducing Neural SDEs has sought to solve. Here, we show that the current classical approach to... | [] | null | 499 | 2102.03657 | title_snapshot |
v139/killamsetty21a | GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training | https://proceedings.mlr.press/v139/killamsetty21a.html | [
"Krishnateja Killamsetty",
"Durga S",
"Ganesh Ramakrishnan",
"Abir De",
"Rishabh Iyer"
] | null | null | The great success of modern machine learning models on large datasets is contingent on extensive computational resources with high financial and environmental costs. One way to address this is by extracting subsets that generalize on par with the full data. In this work, we propose a general framework, GRAD-MATCH, whic... | [] | null | 500 | 2103.00123 | title_snapshot |
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