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Creators/Authors contains: "Zhou, Dongruo"

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  1. ; ; (, International Conference on Learning Representations (ICLR) 2025)
    We investigate the problem of batched best arm identification in multi-armed bandits, where we aim to identify the best arm from a set of n arms while minimizing both the number of samples and batches. We introduce an algorithm that achieves near-optimal sample complexity and features an instance-sensitive batch complexity, which breaks the log(1/Δ_2) barrier. The main contribution of our algorithm is a novel sample allocation scheme that effectively balances exploration and exploitation for batch sizes. Experimental results indicate that our approach is more batch-efficient across various setups. We also extend this framework to the problem of batched best arm identification in linear bandits and achieve similar improvements. 
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    Free, publicly-accessible full text available April 1, 2026
  2. ; ; ; (, International Conference on Machine Learning)
    Accepted Manuscript Full Text Available
  3. ; ; ; ; (, International Conference on Learning Representations (ICLR))
    Accepted Manuscript Full Text Available
  4. ; ; ; ; (, International Conference on Learning Representations (ICLR))
    Accepted Manuscript Full Text Available
  5. ; ; ; ; (, Transaction of Machine Learning Research)
    Accepted Manuscript Full Text Available
  6. ; ; ; (, International Conference on Machine Learning (ICML))
    Accepted Manuscript Full Text Available
  7. ; ; ; (, International Conference on Machine Learning (ICML))
    Accepted Manuscript Full Text Available
  8. ; ; ; (, International Conference on Machine Learning (ICML))
    Accepted Manuscript Full Text Available
  9. ; ; ; ; (, Annual Conference on Learning Theory (COLT))
    Accepted Manuscript Full Text Available
  10. ; ; ; ; (, International Conference on Uncertainty in Artificial Intelligence (UAI))
    Accepted Manuscript Full Text Available