4 papers
Multi-Task Representation Learning for Conservative Linear Bandits
Jiabin Lin, Shana Moothedath
This paper presents the Constrained Multi-Task Representation Learning (CMTRL) framework for linear bandits. We consider T linear bandit tasks in a d dimensional space, which share…
Learning Shared Representations for Multi-Task Linear Bandits
Jiabin Lin, Shana Moothedath
Multi-task representation learning is an approach that learns shared latent representations across related tasks, facilitating knowledge transfer and improving sample efficiency. T…
Distributed Multi-Task Learning for Stochastic Bandits with Context Distribution and Stage-wise Constraints
Jiabin Lin, Shana Moothedath
We present conservative distributed multi-task learning in stochastic linear contextual bandits with heterogeneous agents. This extends conservative linear bandits to a distributed…
Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits
Jiabin Lin, Shana Moothedath, Namrata Vaswani
We study how representation learning can improve the learning efficiency of contextual bandit problems. We study the setting where we play T contextual linear bandits with dimensio…