7 citations · 18 across the 29 of their papers we have counts for
4 papers · 1 filter
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…
Provable Multi-Task Reinforcement Learning: A Representation Learning Framework with Low Rank Rewards
Yaoze Guo, Shana Moothedath
Multi-task representation learning (MTRL) is an approach that learns shared latent representations across related tasks, facilitating collaborative learning that improves the overa…
RepUCB: Representation Learning-Based UCB for Heterogeneous Multi-Task Linear Bandits
Jiabin Lin, Shana Moothedath
Multi-task representation learning exploits the shared structure among related tasks by learning a common latent representation, thereby improving sample efficiency. This paper int…
Post-Decision State-Based Online Learning for Delay-Energy-Aware Flow Allocation in Wireless Systems
Mahesh Ganesh Bhat, Shana Moothedath, Prasanna Chaporkar
We develop a structure-aware reinforcement learning (RL) approach for delay- and energy-aware flow allocation in 5G User Plane Functions (UPFs). We consider a dynamic system with $…