Csaba Szepesvari

research

∙ 06/13/2022

Near-Optimal Sample Complexity Bounds for Constrained MDPs

In contrast to the advances in characterizing the sample complexity for ...

Sharan Vaswani, et al. ∙

research

∙ 06/05/2022

Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization

Directed Evolution (DE), a landmark wet-lab method originated in 1960s, ...

Hui Yuan, et al. ∙

research

∙ 06/02/2022

Sample-Efficient Reinforcement Learning of Partially Observable Markov Games

This paper considers the challenging tasks of Multi-Agent Reinforcement ...

Qinghua Liu, et al. ∙

research

∙ 05/27/2022

KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal

In this work, we consider and analyze the sample complexity of model-fre...

Tadashi Kozuno, et al. ∙

research

∙ 04/19/2022

When Is Partially Observable Reinforcement Learning Not Scary?

Applications of Reinforcement Learning (RL), in which agents learn to ma...

Qinghua Liu, et al. ∙

research

∙ 04/11/2022

Towards Painless Policy Optimization for Constrained MDPs

We study policy optimization in an infinite horizon, γ-discounted constr...

Arushi Jain, et al. ∙

research

∙ 11/22/2021

A Free Lunch from the Noise: Provable and Practical Exploration for Representation Learning

Representation learning lies at the heart of the empirical success of de...

Tongzheng Ren, et al. ∙

research

∙ 10/29/2021

Understanding the Effect of Stochasticity in Policy Optimization

We study the effect of stochasticity in on-policy policy optimization, a...

Jincheng Mei, et al. ∙

research

∙ 10/05/2021

TensorPlan and the Few Actions Lower Bound for Planning in MDPs under Linear Realizability of Optimal Value Functions

We consider the minimax query complexity of online planning with a gener...

Gellért Weisz, et al. ∙

research

∙ 08/12/2021

Efficient Local Planning with Linear Function Approximation

We study query and computationally efficient planning algorithms with li...

Dong Yin, et al. ∙

research

∙ 07/27/2021

On the Role of Optimization in Double Descent: A Least Squares Study

Empirically it has been observed that the performance of deep neural net...

Ilja Kuzborskij, et al. ∙

research

∙ 07/13/2021

No Regrets for Learning the Prior in Bandits

We propose AdaTS, a Thompson sampling algorithm that adapts sequentially...

Soumya Basu, et al. ∙

research

∙ 07/12/2021

Nonparametric Regression with Shallow Overparameterized Neural Networks Trained by GD with Early Stopping

We explore the ability of overparameterized shallow neural networks to l...

Ilja Kuzborskij, et al. ∙

research

∙ 06/18/2021

On the Sample Complexity of Batch Reinforcement Learning with Policy-Induced Data

We study the fundamental question of the sample complexity of learning a...

Chenjun Xiao, et al. ∙

research

∙ 06/15/2021

On Multi-objective Policy Optimization as a Tool for Reinforcement Learning

Many advances that have improved the robustness and efficiency of deep r...

Abbas Abdolmaleki, et al. ∙

research

∙ 05/13/2021

Leveraging Non-uniformity in First-order Non-convex Optimization

Classical global convergence results for first-order methods rely on uni...

Jincheng Mei, et al. ∙

research

∙ 04/06/2021

On the Optimality of Batch Policy Optimization Algorithms

Batch policy optimization considers leveraging existing data for policy ...

Chenjun Xiao, et al. ∙

research

∙ 02/25/2021

Improved Regret Bound and Experience Replay in Regularized Policy Iteration

In this work, we study algorithms for learning in infinite-horizon undis...

Nevena Lazic, et al. ∙

research

∙ 02/17/2021

On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method

Policy gradient gives rise to a rich class of reinforcement learning (RL...

Junyu Zhang, et al. ∙

research

∙ 02/11/2021

Optimization Issues in KL-Constrained Approximate Policy Iteration

Many reinforcement learning algorithms can be seen as versions of approx...

Nevena Lazic, et al. ∙

research

∙ 02/11/2021

Meta-Thompson Sampling

Efficient exploration in multi-armed bandits is a fundamental online lea...

Branislav Kveton, et al. ∙

research

∙ 02/06/2021

Bootstrapping Statistical Inference for Off-Policy Evaluation

Bootstrapping provides a flexible and effective approach for assessing t...

Botao Hao, et al. ∙

research

∙ 02/03/2021

On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function

We consider the problem of local planning in fixed-horizon Markov Decisi...

Gellért Weisz, et al. ∙

research

∙ 12/15/2020

Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes

We study reinforcement learning (RL) with linear function approximation ...

Dongruo Zhou, et al. ∙

research

∙ 11/11/2020

Asymptotically Optimal Information-Directed Sampling

We introduce a computationally efficient algorithm for finite stochastic...

Johannes Kirschner, et al. ∙

research

∙ 11/08/2020

Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient

This paper provides a statistical analysis of high-dimensional batch Rei...

Botao Hao, et al. ∙

research

∙ 11/08/2020

Online Sparse Reinforcement Learning

We investigate the hardness of online reinforcement learning in fixed ho...

Botao Hao, et al. ∙

research

∙ 10/31/2020

On Optimality of Meta-Learning in Fixed-Design Regression with Weighted Biased Regularization

We consider a fixed-design linear regression in the meta-learning model ...

Mikhail Konobeev, et al. ∙

research

∙ 10/23/2020

Online Algorithm for Unsupervised Sequential Selection with Contextual Information

In this paper, we study Contextual Unsupervised Sequential Selection (US...

Arun Verma, et al. ∙

research

∙ 10/22/2020

CoinDICE: Off-Policy Confidence Interval Estimation

We study high-confidence behavior-agnostic off-policy evaluation in rein...

Bo Dai, et al. ∙

research

∙ 10/03/2020

Exponential Lower Bounds for Planning in MDPs With Linearly-Realizable Optimal Action-Value Functions

We consider the problem of local planning in fixed-horizon Markov Decisi...

Gellért Weisz, et al. ∙

research

∙ 07/25/2020

Tighter risk certificates for neural networks

This paper presents empirical studies regarding training probabilistic n...

Maria Perez-Ortiz, et al. ∙

research

∙ 07/13/2020

Efficient Planning in Large MDPs with Weak Linear Function Approximation

Large-scale Markov decision processes (MDPs) require planning algorithms...

Roshan Shariff, et al. ∙

research

∙ 07/04/2020

Variational Policy Gradient Method for Reinforcement Learning with General Utilities

In recent years, reinforcement learning (RL) systems with general goals ...

Junyu Zhang, et al. ∙

research

∙ 06/23/2020

PAC-Bayes Analysis Beyond the Usual Bounds

We focus on a stochastic learning model where the learner observes a fin...

Omar Rivasplata, et al. ∙

research

∙ 06/18/2020

Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting

We consider off-policy evaluation in the contextual bandit setting for t...

Ilja Kuzborskij, et al. ∙

research

∙ 06/09/2020

Differentiable Meta-Learning in Contextual Bandits

We study a contextual bandit setting where the learning agent has access...

Branislav Kveton, et al. ∙

research

∙ 06/01/2020

Model-Based Reinforcement Learning with Value-Targeted Regression

This paper studies model-based reinforcement learning (RL) for regret mi...

Alex Ayoub, et al. ∙

research

∙ 05/13/2020

On the Global Convergence Rates of Softmax Policy Gradient Methods

We make three contributions toward better understanding policy gradient ...

Jincheng Mei, et al. ∙

research

∙ 03/03/2020

Model Selection in Contextual Stochastic Bandit Problems

We study model selection in stochastic bandit problems. Our approach rel...

Aldo Pacchiano, et al. ∙

research

∙ 02/17/2020

Differentiable Bandit Exploration

We learn bandit policies that maximize the average reward over bandit in...

Craig Boutilier, et al. ∙

research

∙ 02/08/2020

Provably Efficient Adaptive Approximate Policy Iteration

Model-free reinforcement learning algorithms combined with value functio...

Botao Hao, et al. ∙

research

∙ 11/18/2019

Learning with Good Feature Representations in Bandits and in RL with a Generative Model

The construction in the recent paper by Du et al. [2019] implies that se...

Tor Lattimore, et al. ∙

research

∙ 10/18/2019

Autonomous exploration for navigating in non-stationary CMPs

We consider a setting in which the objective is to learn to navigate in ...

Pratik Gajane, et al. ∙

research

∙ 10/15/2019

Adaptive Exploration in Linear Contextual Bandit

Contextual bandits serve as a fundamental model for many sequential deci...

Botao Hao, et al. ∙

research

∙ 09/04/2019

Efron-Stein PAC-Bayesian Inequalities

We prove semi-empirical concentration inequalities for random variables ...

Ilja Kuzborskij, et al. ∙

research

∙ 08/27/2019

Exploration-Enhanced POLITEX

We study algorithms for average-cost reinforcement learning problems wit...

Yasin Abbasi-Yadkori, et al. ∙

research

∙ 08/19/2019

PAC-Bayes with Backprop

We explore a method to train probabilistic neural networks by minimizing...

Omar Rivasplata, et al. ∙

research

∙ 07/12/2019

Exploration by Optimisation in Partial Monitoring

We provide a simple and efficient algorithm for adversarial k-action d-o...

Tor Lattimore, et al. ∙

research

∙ 06/21/2019

Randomized Exploration in Generalized Linear Bandits

We study two randomized algorithms for generalized linear bandits, GLM-T...

Branislav Kveton, et al. ∙

Csaba Szepesvari

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