research
∙
05/22/2023
Offline Primal-Dual Reinforcement Learning for Linear MDPs
Offline Reinforcement Learning (RL) aims to learn a near-optimal policy ...
share
research
∙
10/24/2022
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees
We study the problem of representation learning in stochastic contextual...
research
∙
07/18/2022
Online Learning with Off-Policy Feedback
We study the problem of online learning in adversarial bandit problems u...
research
∙
05/27/2022
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits
We study the Bayesian regret of the renowned Thompson Sampling algorithm...
research
∙
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 ...
research
∙
04/08/2021
Leveraging Good Representations in Linear Contextual Bandits
The linear contextual bandit literature is mostly focused on the design ...
research
∙
12/15/2020
Policy Optimization as Online Learning with Mediator Feedback
Policy Optimization (PO) is a widely used approach to address continuous...
research
∙
12/06/2019
Risk-Averse Trust Region Optimization for Reward-Volatility Reduction
In real-world decision-making problems, for instance in the fields of fi...
research
∙
09/09/2019
Gradient-Aware Model-based Policy Search
Traditional model-based reinforcement learning approaches learn a model ...
research
∙
07/17/2019
Feature Selection via Mutual Information: New Theoretical Insights
Mutual information has been successfully adopted in filter feature-selec...
research
∙
05/08/2019
Smoothing Policies and Safe Policy Gradients
Policy gradient algorithms are among the best candidates for the much an...
research
∙
09/17/2018
Policy Optimization via Importance Sampling
Policy optimization is an effective reinforcement learning approach to s...
research
∙
06/14/2018