From the 1 of 4 linked papers with an AI index.
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Active Exploration via Autoregressive Generation of Missing Data
Tiffany Tianhui Cai, Hongseok Namkoong, Daniel Russo +1
The paper proposes using autoregressive sequence models to quantify uncertainty and guide exploration in online decision-making, showing that Bayesian regret can be bounded by offl…
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…
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…