In the stochastic contextual low-rank matrix bandit problem, the expected reward of an action
is given by the inner product between the action's feature matrix and some fixed, but initially …

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 …

We study matrix estimation problems arising in reinforcement learning with low-rank
structure. In low-rank bandits, the matrix to be recovered specifies the expected arm …

We study multi-task representation learning for the problem of pure exploration in bilinear
bandits. In bilinear bandits, an action takes theform of a pair of arms from two different entity …

We study how representation learning can improve the efficiency of bandit problems. We
study the setting where we play $ T $ linear bandits with dimension $ d $ concurrently, and …

In a low-rank linear bandit problem, the reward of an action (represented by a matrix of size
$ d_1\times d_2 $) is the inner product between the action and an unknown low-rank matrix …

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 …

Key challenges in running a retail business include how to select products to present to
consumers (the assortment problem), and how to price products (the pricing problem) to …

We give novel algorithms for multi-task and lifelong linear bandits with shared
representation. Specifically, we consider the setting where we play $ M $ linear bandits with …

We consider the problem of latent bandits with cluster structure where there are multiple
users, each with an associated multi-armed bandit problem. These users are grouped into …