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06/22/2023
Context-lumpable stochastic bandits
We consider a contextual bandit problem with S contexts and A actions. I...
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02/25/2022
Non-stationary Bandits and Meta-Learning with a Small Set of Optimal Arms
We study a sequential decision problem where the learner faces a sequenc...
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01/17/2022
A New Look at Dynamic Regret for Non-Stationary Stochastic Bandits
We study the non-stationary stochastic multi-armed bandit problem, where...
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08/12/2021
Efficient Local Planning with Linear Function Approximation
We study query and computationally efficient planning algorithms with li...
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06/09/2021
Parameter and Feature Selection in Stochastic Linear Bandits
We study two model selection settings in stochastic linear bandits (LB)....
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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...
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02/11/2021
Optimization Issues in KL-Constrained Approximate Policy Iteration
Many reinforcement learning algorithms can be seen as versions of approx...
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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...
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10/20/2020
The Elliptical Potential Lemma Revisited
This note proposes a new proof and new perspectives on the so-called Ell...
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06/09/2020
Regret Balancing for Bandit and RL Model Selection
We consider model selection in stochastic bandit and reinforcement learn...
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06/04/2020
Sample Efficient Graph-Based Optimization with Noisy Observations
We study sample complexity of optimizing "hill-climbing friendly" functi...
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03/03/2020
Model Selection in Contextual Stochastic Bandit Problems
We study model selection in stochastic bandit problems. Our approach rel...
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02/08/2020
Provably Efficient Adaptive Approximate Policy Iteration
Model-free reinforcement learning algorithms combined with value functio...
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08/27/2019
Exploration-Enhanced POLITEX
We study algorithms for average-cost reinforcement learning problems wit...
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08/14/2019
Thompson Sampling and Approximate Inference
We study the effects of approximate inference on the performance of Thom...
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06/12/2019
Bootstrapping Upper Confidence Bound
Upper Confidence Bound (UCB) method is arguably the most celebrated one ...
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01/06/2019
Large-Scale Markov Decision Problems via the Linear Programming Dual
We consider the problem of controlling a fully specified Markov decision...
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05/24/2018
New Insights into Bootstrapping for Bandits
We investigate the use of bootstrapping in the bandit setting. We first ...
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05/04/2018
Sharp Convergence Rates for Langevin Dynamics in the Nonconvex Setting
We study the problem of sampling from a distribution where the negative ...
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04/27/2018
Offline Evaluation of Ranking Policies with Click Models
Many web systems rank and present a list of items to users, from recomme...
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04/17/2018
Regret Bounds for Model-Free Linear Quadratic Control
Model-free approaches for reinforcement learning (RL) and continuous con...
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02/26/2018
Optimizing over a Restricted Policy Class in Markov Decision Processes
We address the problem of finding an optimal policy in a Markov decision...
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02/10/2018
A Continuation Method for Discrete Optimization and its Application to Nearest Neighbor Classification
The continuation method is a popular approach in non-convex optimization...
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12/13/2017
Stochastic Low-Rank Bandits
Many problems in computer vision and recommender systems involve low-ran...
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11/21/2017
Posterior Sampling for Large Scale Reinforcement Learning
Posterior sampling for reinforcement learning (PSRL) is a popular algori...
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11/19/2016
Conservative Contextual Linear Bandits
Safety is a desirable property that can immensely increase the applicabi...
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10/19/2016
Hit-and-Run for Sampling and Planning in Non-Convex Spaces
We propose the Hit-and-Run algorithm for planning and sampling problems ...
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06/26/2014
Online learning in MDPs with side information
We study online learning of finite Markov decision process (MDP) problem...
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02/27/2014
Linear Programming for Large-Scale Markov Decision Problems
We consider the problem of controlling a Markov decision process (MDP) w...
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03/12/2013
Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions
We study the problem of learning Markov decision processes with finite s...
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05/09/2012
Improved Mean and Variance Approximations for Belief Net Responses via Network Doubling
A Bayesian belief network models a joint distribution with an directed a...
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02/14/2011