7 citations · 29 across the 42 of their papers we have counts for
4 papers · 1 filter
Hybrid Preference Optimization for Alignment: Provably Faster Convergence Rates by Combining Offline Preferences with Online Exploration
Avinandan Bose, Zhihan Xiong, Aadirupa Saha +2
Reinforcement Learning from Human Feedback (RLHF) is currently the leading approach for aligning large language models with human preferences. Typically, these models rely on exten…
Strategic Linear Contextual Bandits
Thomas Kleine Buening, Aadirupa Saha, Christos Dimitrakakis +1
Motivated by the phenomenon of strategic agents gaming a recommender system to maximize the number of times they are recommended to users, we study a strategic variant of the linea…
DP-Dueling: Learning from Preference Feedback without Compromising User Privacy
Aadirupa Saha, Hilal Asi
We consider the well-studied dueling bandit problem, where a learner aims to identify near-optimal actions using pairwise comparisons, under the constraint of differential privacy.…
Stop Relying on No-Choice and Do not Repeat the Moves: Optimal, Efficient and Practical Algorithms for Assortment Optimization
Aadirupa Saha, Pierre Gaillard
We address the problem of active online assortment optimization problem with preference feedback, which is a framework for modeling user choices and subsetwise utility maximization…