This paper studies representation learning for multi-task linear bandits and multi-task
episodic RL with linear value function approximation. We first consider the setting where we …

This paper studies model-based bandit and reinforcement learning (RL) with nonlinear
function approximations. We propose to study convergence to approximate local maxima …

Contextual bandits are a rich model for sequential decision making given side information,
with important applications, eg, in recommender systems. We propose novel algorithms for …

In sparse linear bandits, a learning agent sequentially selects an action from a fixed action
set and receives reward feedback, and the reward function depends linearly on a few …

A long line of works characterizes the sample complexity of regret minimization in sequential
decision-making by min-max programs. In the corresponding saddle-point game, the min …

Abstract Information-directed sampling (IDS) has recently demonstrated its potential as a
data-efficient reinforcement learning algorithm. However, it is still unclear what is the right …

We study the problem of transfer-learning in the setting of stochastic linear contextual bandit
tasks. We consider that a low dimensional linear representation is shared across the tasks …

We propose a new regret minimization algorithm for episodic sparse linear Markov decision
process (SMDP) where the state-transition distribution is a linear function of observed …

Stochastic high dimensional bandit problems with low dimensional structures are useful in
different applications such as online advertising and drug discovery. In this work, we …

Abstract Model selection in the context of bandit optimization is a challenging problem, as it
requires balancing exploration and exploitation not only for action selection, but also for …