11 citations · 29 across the 13 of their papers we have counts for
16 papers
Exponential Reward Weighting for Fine-Tuning Generative Recommenders under Sparse and Noisy Feedback
Keertana Chidambaram, Sanath Kumar Krishnamurthy, Qiuling Xu +2
In recommendation systems, users interact with only a small fraction of a vast item catalog, producing feedback that is both sparse and noisy. This challenges post-training generat…
Robust Post-Training for Generative Recommenders: Why Exponential Reward-Weighted SFT Outperforms RLHF
Keertana Chidambaram, Sanath Kumar Krishnamurthy, Qiuling Xu +2
Aligning generative recommender systems to user preferences via post-training is critical for closing the gap between next-item prediction and actual recommendation quality. Existi…
Adaptive Exploration for Latent-State Bandits
Jikai Jin, Kenneth Hung, Sanath Kumar Krishnamurthy +2
We study bandits whose rewards depend on an unobserved Markov state that evolves independently of the learner's actions. The optimal arm can change even though the learner observes…
Data-driven Error Estimation: Excess Risk Bounds without Class Complexity as Input
Sanath Kumar Krishnamurthy, Anna Lyubarskaja, Emma Brunskill +1
Constructing confidence intervals that are simultaneously valid across a class of estimates is central to tasks such as multiple mean estimation, generalization guarantees, and ada…
Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization
Sanath Kumar Krishnamurthy, Ruohan Zhan, Susan Athey +1
In many applications, e.g. in healthcare and e-commerce, the goal of a contextual bandit may be to learn an optimal treatment assignment policy at the end of the experiment. That i…
Selective Uncertainty Propagation in Offline RL
Sanath Kumar Krishnamurthy, Tanmay Gangwani, Sumeet Katariya +3
We consider the finite-horizon offline reinforcement learning (RL) setting, and are motivated by the challenge of learning the policy at any step h in dynamic programming (DP) algo…