12 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…
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…
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…
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 $…
Diffusion-based Decentralized Federated Multi-Task Representation Learning
Donghwa Kang, Shana Moothedath
Representation learning is a widely adopted framework for learning in data-scarce environments to obtain a feature extractor or representation from various different yet related ta…
Beyond Centralization: Provable Communication Efficient Decentralized Multi-Task Learning
Donghwa Kang, Shana Moothedath
Representation learning is a widely adopted framework for learning in data-scarce environments, aiming to extract common features from related tasks. While centralized approaches h…