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cs.LG2024★ 1 cited
Linear Contextual Bandits with Hybrid Payoff: Revisited
Nirjhar Das, Gaurav Sinha
We study the Linear Contextual Bandit problem in the hybrid reward setting. In this setting every arm's reward model contains arm specific parameters in addition to parameters shar…
cs.LG2024
Generalized Linear Bandits with Limited Adaptivity
Ayush Sawarni, Nirjhar Das, Siddharth Barman +1
We study the generalized linear contextual bandit problem within the constraints of limited adaptivity. In this paper, we present two algorithms, and $\texttt{R…
cs.LG2024
Active Preference Optimization for Sample Efficient RLHF
Nirjhar Das, Souradip Chakraborty, Aldo Pacchiano +1
Large Language Models (LLMs) aligned using Reinforcement Learning from Human Feedback (RLHF) have shown remarkable generation abilities in numerous tasks. However, collecting high-…