7 citations · 29 across the 42 of their papers we have counts for
5 papers · 1 filter
Replicable Dueling Bandits
Rohan Deb, Aadirupa Saha, Udaya Ghai +2
Dueling bandits choose each comparison based on earlier noisy outcomes, so two runs on the same preferences can compare different pairs. We study replicable dueling bandits, where…
Bandit PCA with Minimax Optimal Regret
Moïse Blanchard, Dmitrii Ostrovskii, Aadirupa Saha
We study the bandit-feedback version of online principal component analysis (Bandit PCA): in each round , the adversary selects a symmetric gain matrix…
Learning to Allocate Resources with Censored Feedback
Giovanni Montanari, Côme Fiegel, Corentin Pla +2
We study an online resource allocation problem in which at each round, a budget must be allocated across arms under censored feedback. An arm yields a reward if and only if…
LLM-as-Judge on a Budget
Aadirupa Saha, Aniket Wagde, Branislav Kveton
LLM-as-a-judge has emerged as a cornerstone technique for evaluating large language models by leveraging LLM reasoning to score prompt-response pairs. Since LLM judgments are stoch…
One Good Source is All You Need: Near-Optimal Regret for Bandits under Heterogeneous Noise
Amith Bhat, Haipeng Luo, Aadirupa Saha
We study -armed Multiarmed Bandit (MAB) problem with heterogeneous data sources, each exhibiting unknown and distinct noise variances . The learner's obje…