1 citations · 1 across the 5 of their papers we have counts for
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Contextual Thompson Sampling via Generation of Missing Data
Kelly W. Zhang, Tiffany Tianhui Cai, Hongseok Namkoong +1
We introduce a framework for Thompson sampling (TS) contextual bandit algorithms, in which the algorithm's ability to quantify uncertainty and make decisions depends on the quality…
Impatient Bandits: Optimizing for the Long-Term Without Delay
Kelly W. Zhang, Thomas Baldwin-McDonald, Kamil Ciosek +2
Increasingly, recommender systems are tasked with improving users' long-term satisfaction. In this context, we study a content exploration task, which we formalize as a bandit prob…
Active Exploration via Autoregressive Generation of Missing Data
Tiffany Tianhui Cai, Hongseok Namkoong, Daniel Russo +1
We pose uncertainty quantification and exploration in online decision-making as a problem of training and generation from an autoregressive sequence model, an area experiencing rap…
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…