7 citations · 18 across the 29 of their papers we have counts for
13 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…
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…
Neural Contextual Bandits Under Delayed Feedback Constraints
Mohammadali Moghimi, Sharu Theresa Jose, Shana Moothedath
This paper presents a new algorithm for neural contextual bandits (CBs) that addresses the challenge of delayed reward feedback, where the reward for a chosen action is revealed af…
Byzantine Resilient Federated Multi-Task Representation Learning
Tuan Le, Shana Moothedath
In this paper, we propose BR-MTRL, a Byzantine-resilient multi-task representation learning framework that handles faulty or malicious agents. Our approach leverages representation…
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…
Thompson Sampling for Stochastic Bandits with Noisy Contexts: An Information-Theoretic Regret Analysis
Sharu Theresa Jose, Shana Moothedath
We explore a stochastic contextual linear bandit problem where the agent observes a noisy, corrupted version of the true context through a noise channel with an unknown noise param…
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…
Federated Learning for Heterogeneous Bandits with Unobserved Contexts
Jiabin Lin, Shana Moothedath
We study the problem of federated stochastic multi-arm contextual bandits with unknown contexts, in which M agents are faced with different bandits and collaborate to learn. The co…
Distributed Stochastic Bandit Learning with Delayed Context Observation
Jiabin Lin, Shana Moothedath
We consider the problem where M agents collaboratively interact with an instance of a stochastic K-armed contextual bandit, where K>>M. The goal of the agents is to simultaneously…
Stochastic Conservative Contextual Linear Bandits
Jiabin Lin, Xian Yeow Lee, Talukder Jubery +3
Many physical systems have underlying safety considerations that require that the strategy deployed ensures the satisfaction of a set of constraints. Further, often we have only pa…