7 citations · 29 across the 42 of their papers we have counts for
6 papers · 1 filter
One Arrow, Two Kills: An Unified Framework for Achieving Optimal Regret Guarantees in Sleeping Bandits
Pierre Gaillard, Aadirupa Saha, Soham Dan
We address the problem of \emph{`Internal Regret'} in \emph{Sleeping Bandits} in the fully adversarial setup, as well as draw connections between different existing notions of slee…
ANACONDA: An Improved Dynamic Regret Algorithm for Adaptive Non-Stationary Dueling Bandits
Thomas Kleine Buening, Aadirupa Saha
We study the problem of non-stationary dueling bandits and provide the first adaptive dynamic regret algorithm for this problem. The only two existing attempts in this line of work…
Dueling Convex Optimization with General Preferences
Aadirupa Saha, Tomer Koren, Yishay Mansour
We address the problem of \emph{convex optimization with dueling feedback}, where the goal is to minimize a convex function given a weaker form of \emph{dueling} feedback. Each que…
Exploiting Correlation to Achieve Faster Learning Rates in Low-Rank Preference Bandits
Suprovat Ghoshal, Aadirupa Saha
We introduce the \emph{Correlated Preference Bandits} problem with random utility-based choice models (RUMs), where the goal is to identify the best item from a given pool of i…
Versatile Dueling Bandits: Best-of-both-World Analyses for Online Learning from Preferences
Aadirupa Saha, Pierre Gaillard
We study the problem of -armed dueling bandit for both stochastic and adversarial environments, where the goal of the learner is to aggregate information through relative prefer…
Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models
Viktor Bengs, Aadirupa Saha, Eyke Hüllermeier
We consider the regret minimization task in a dueling bandits problem with context information. In every round of the sequential decision problem, the learner makes a context-depen…