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Creators/Authors contains: "Peng, Binghui"

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  1. ; (, PMLR)
    Accepted Manuscript Full Text Available
  2. ; (, International Conference on Algorithmic Learning Theory)
    Accepted Manuscript Full Text Available
  3. ; ; (, Collegium Beatus Rhenanus)
    Accepted Manuscript Full Text Available
  4. ; (, Proceedings of the 64th IEEE Symposium on Foundations of Computer Science)
  5. ; (, ACM)
    Full Text Available 
  6. ; (, Games and Economic Behavior)
    Full Text Available 
  7. ; (, Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA))
    We provide the first sub-linear space and sub-linear regret algorithm for online learning with expert advice (against an oblivious adversary), addressing an open question raised recently by Srinivas, Woodruff, Xu and Zhou (STOC 2022). We also demonstrate a separation between oblivious and (strong) adaptive adversaries by proving a linear memory lower bound of any sub-linear regret algorithm against an adaptive adversary. Our algorithm is based on a novel pool selection procedure that bypasses the traditional wisdom of leader selection for online learning, and a generic reduction that transforms any weakly sub-linear regret o(T) algorithm to T1-α regret algorithm, which may be of independent interest. Our lower bound utilizes the connection of no-regret learning and equilibrium computation in zero-sum games, leading to a proof of a strong lower bound against an adaptive adversary. 
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  8. ; ; (, In Proceedings of the 63rd IEEE Symposium on Foundations of Computer Science (FOCS))
    Full Text Available 
  9. ; (, Proceedings of the 54th ACM Symposium on Theory of Computing (STOC 22'))
    Full Text Available 
  10. ; ; ; (, Annual ACM-SIAM Symposium on Discrete Algorithms)
    Full Text Available