33 citations · 51 across the 14 of their papers we have counts for
7 papers · 1 filter
Cover meets Robbins while Betting on Bounded Data: Regret and Almost Sure Regret
Shubhada Agrawal, Aaditya Ramdas
Consider betting against a sequence of -valued observations using bets that are fair if the data have a conditional mean . Cover's universal portfolio algorit…
Eventually LIL Regret: Almost Sure Regret for a sub-Gaussian Mixture on Unbounded Data
Shubhada Agrawal, Aaditya Ramdas
We prove that a classic sub-Gaussian mixture proposed by Robbins in a stochastic setting actually satisfies a path-wise (deterministic) regret bound. For every path in a natural ``…
Optimal Top-Two Method for Best Arm Identification and Fluid Analysis
Agniv Bandyopadhyay, Sandeep Juneja, Shubhada Agrawal
Top- methods have become popular in solving the best arm identification (BAI) problem. The best arm, or the arm with the largest mean amongst finitely many, is identified throug…
Optimal Best-Arm Identification in Bandits with Access to Offline Data
Shubhada Agrawal, Sandeep Juneja, Karthikeyan Shanmugam +1
Learning paradigms based purely on offline data as well as those based solely on sequential online learning have been well-studied in the literature. In this paper, we consider com…
Regret Minimization in Heavy-Tailed Bandits
Shubhada Agrawal, Sandeep Juneja, Wouter M. Koolen
We revisit the classic regret-minimization problem in the stochastic multi-armed bandit setting when the arm-distributions are allowed to be heavy-tailed. Regret minimization has b…
Optimal Best-Arm Identification Methods for Tail-Risk Measures
Shubhada Agrawal, Wouter M. Koolen, Sandeep Juneja
Conditional value-at-risk (CVaR) and value-at-risk (VaR) are popular tail-risk measures in finance and insurance industries as well as in highly reliable, safety-critical uncertain…
Optimal -Correct Best-Arm Selection for Heavy-Tailed Distributions
Shubhada Agrawal, Sandeep Juneja, Peter Glynn
Given a finite set of unknown distributions or arms that can be sampled, we consider the problem of identifying the one with the maximum mean using a -correct algorithm (an adap…