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02/07/2023
Layered State Discovery for Incremental Autonomous Exploration
We study the autonomous exploration (AX) problem proposed by Lim Aue...
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12/19/2022
On the Complexity of Representation Learning in Contextual Linear Bandits
In contextual linear bandits, the reward function is assumed to be a lin...
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11/04/2022
Improved Adaptive Algorithm for Scalable Active Learning with Weak Labeler
Active learning with strong and weak labelers considers a practical sett...
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10/24/2022
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees
We study the problem of representation learning in stochastic contextual...
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10/18/2022
Contextual bandits with concave rewards, and an application to fair ranking
We consider Contextual Bandits with Concave Rewards (CBCR), a multi-obje...
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10/10/2022
Reaching Goals is Hard: Settling the Sample Complexity of the Stochastic Shortest Path
We study the sample complexity of learning an ϵ-optimal policy in the St...
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12/13/2021
Top K Ranking for Multi-Armed Bandit with Noisy Evaluations
We consider a multi-armed bandit setting where, at the beginning of each...
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12/11/2021
Privacy Amplification via Shuffling for Linear Contextual Bandits
Contextual bandit algorithms are widely used in domains where it is desi...
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12/02/2021
Differentially Private Exploration in Reinforcement Learning with Linear Representation
This paper studies privacy-preserving exploration in Markov Decision Pro...
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11/23/2021
Adaptive Multi-Goal Exploration
We introduce a generic strategy for provably efficient multi-goal explor...
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10/27/2021
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection
We study the role of the representation of state-action value functions ...
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06/24/2021
A Fully Problem-Dependent Regret Lower Bound for Finite-Horizon MDPs
We derive a novel asymptotic problem-dependent lower-bound for regret mi...
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06/22/2021
A Unified Framework for Conservative Exploration
We study bandits and reinforcement learning (RL) subject to a conservati...
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04/22/2021
Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret
We study the problem of learning in the stochastic shortest path (SSP) s...
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04/08/2021
Leveraging Good Representations in Linear Contextual Bandits
The linear contextual bandit literature is mostly focused on the design ...
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03/17/2021
Homomorphically Encrypted Linear Contextual Bandit
Contextual bandit is a general framework for online learning in sequenti...
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12/29/2020
Improved Sample Complexity for Incremental Autonomous Exploration in MDPs
We investigate the exploration of an unknown environment when no reward ...
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10/23/2020
An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits
In the contextual linear bandit setting, algorithms built on the optimis...
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10/15/2020
Local Differentially Private Regret Minimization in Reinforcement Learning
Reinforcement learning algorithms are widely used in domains where it is...
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07/13/2020
A Provably Efficient Sample Collection Strategy for Reinforcement Learning
A common assumption in reinforcement learning (RL) is to have access to ...
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07/10/2020
Improved Analysis of UCRL2 with Empirical Bernstein Inequality
We consider the problem of exploration-exploitation in communicating Mar...
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07/09/2020
A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces
In this work, we propose KeRNS: an algorithm for episodic reinforcement ...
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05/06/2020
Learning Adaptive Exploration Strategies in Dynamic Environments Through Informed Policy Regularization
We study the problem of learning exploration-exploitation strategies tha...
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04/12/2020
Regret Bounds for Kernel-Based Reinforcement Learning
We consider the exploration-exploitation dilemma in finite-horizon reinf...
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03/06/2020
Active Model Estimation in Markov Decision Processes
We study the problem of efficient exploration in order to learn an accur...
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03/04/2020
Exploration-Exploitation in Constrained MDPs
In many sequential decision-making problems, the goal is to optimize a u...
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02/10/2020
Adversarial Attacks on Linear Contextual Bandits
Contextual bandit algorithms are applied in a wide range of domains, fro...
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02/08/2020
Improved Algorithms for Conservative Exploration in Bandits
In many fields such as digital marketing, healthcare, finance, and robot...
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02/08/2020
Conservative Exploration in Reinforcement Learning
While learning in an unknown Markov Decision Process (MDP), an agent sho...
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01/30/2020
Concentration Inequalities for Multinoulli Random Variables
We investigate concentration inequalities for Dirichlet and Multinomial ...
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01/13/2020
Exploiting Language Instructions for Interpretable and Compositional Reinforcement Learning
In this work, we present an alternative approach to making an agent comp...
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12/07/2019
No-Regret Exploration in Goal-Oriented Reinforcement Learning
Many popular reinforcement learning problems (e.g., navigation in a maze...
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11/01/2019
Frequentist Regret Bounds for Randomized Least-Squares Value Iteration
We consider the exploration-exploitation dilemma in finite-horizon reinf...
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05/08/2019
Smoothing Policies and Safe Policy Gradients
Policy gradient algorithms are among the best candidates for the much an...
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12/11/2018
Exploration Bonus for Regret Minimization in Undiscounted Discrete and Continuous Markov Decision Processes
We introduce and analyse two algorithms for exploration-exploitation in ...
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07/06/2018
Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes
While designing the state space of an MDP, it is common to include state...
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06/14/2018
Stochastic Variance-Reduced Policy Gradient
In this paper, we propose a novel reinforcement- learning algorithm cons...
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05/28/2018
Importance Weighted Transfer of Samples in Reinforcement Learning
We consider the transfer of experience samples (i.e., tuples < s, a, s',...
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02/12/2018
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning
We introduce SCAL, an algorithm designed to perform efficient exploratio...
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12/09/2017
Cost-Sensitive Approach to Batch Size Adaptation for Gradient Descent
In this paper, we propose a novel approach to automatically determine th...
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06/13/2014