1 citations · 1 across the 5 of their papers we have counts for
3 papers
cs.LG2024
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…
cs.LG2022★ 1 cited
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…
cs.DS2021
Fully Decentralized and Federated Low Rank Compressive Sensing
Shana Moothedath, Namrata Vaswani
In this work we develop a fully decentralized, federated, and fast solution to the recently studied Low Rank Compressive Sensing (LRCS) problem: recover an nxq low-rank matrix from…