5 papers
Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning
Yae Jee Cho, Samarth Gupta, Gauri Joshi +1
Due to communication constraints and intermittent client availability in federated learning, only a subset of clients can participate in each training round. While most prior works…
Integer Programming-based Error-Correcting Output Code Design for Robust Classification
Samarth Gupta, Saurabh Amin
Error-Correcting Output Codes (ECOCs) offer a principled approach for combining simple binary classifiers into multiclass classifiers. In this paper, we investigate the problem of…
A Unified Approach to Translate Classical Bandit Algorithms to the Structured Bandit Setting
Samarth Gupta, Shreyas Chaudhari, Subhojyoti Mukherjee +2
We consider a finite-armed structured bandit problem in which mean rewards of different arms are known functions of a common hidden parameter . Since we do not place any restr…
Active Distribution Learning from Indirect Samples
Samarth Gupta, Gauri Joshi, Osman Yağan
This paper studies the problem of {\em learning} the probability distribution of a discrete random variable using indirect and sequential samples. At each time step, we c…
Correlated Multi-armed Bandits with a Latent Random Source
Samarth Gupta, Gauri Joshi, Osman Yağan
We consider a novel multi-armed bandit framework where the rewards obtained by pulling the arms are functions of a common latent random variable. The correlation between arms due t…