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1Cornell University; 2Glance; 3Carnegie Mellon University
TBD
Markovian interference in experiments
Andrew Zheng1, Vivek Farias1, Andrew Li2, Tianyi Peng1
1MIT; 2Carnegie Mellon University
TBD
Diffusion limits of multi-armed bandit experiments under optimism-based policies
Anand Kalvit, Assaf Zeevi
Columbia Business School, United States of America
Our work provides new results on the arm-sampling behavior of the celebrated UCB family of multi-armed bandit algorithms, leading to several important insights. Among these, it is shown that arm-sampling rates under UCB are asymptotically deterministic, regardless of the problem complexity. This discovery facilitates new sharp asymptotic characterizations revealing profound distinctions between UCB and Thompson Sampling such as an "incomplete learning" phenomenon characteristic of the latter.
