57 citations · 71 across the 3 of their papers we have counts for
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cs.LG2024
On the Limited Representational Power of Value Functions and its Links to Statistical (In)Efficiency
David Cheikhi, Daniel Russo
Identifying the trade-offs between model-based and model-free methods is a central question in reinforcement learning. Value-based methods offer substantial computational advantage…
cs.LG2023★ 14 cited
Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay
Thomas M. McDonald, Lucas Maystre, Mounia Lalmas +2
Recommender systems are a ubiquitous feature of online platforms. Increasingly, they are explicitly tasked with increasing users' long-term satisfaction. In this context, we study…
cs.LG2014★ 57 cited
An Information-Theoretic Analysis of Thompson Sampling
Daniel Russo, Benjamin Van Roy
We provide an information-theoretic analysis of Thompson sampling that applies across a broad range of online optimization problems in which a decision-maker must learn from partia…