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- W3132014543 abstract "In this paper, we consider the time-varying Bayesian optimization problem. The unknown function at each time is assumed to lie in an RKHS (reproducing kernel Hilbert space) with a bounded norm. We adopt the general variation budget model to capture the time-varying environment, and the variation is characterized by the change of the RKHS norm. We adapt the restart and sliding window mechanism to introduce two GP-UCB type algorithms: R-GP-UCB and SW-GP-UCB, respectively. We derive the first (frequentist) regret guarantee on the dynamic regret for both algorithms. Our results not only recover previous linear bandit results when a linear kernel is used, but complement the previous regret analysis of time-varying Gaussian process bandit under a Bayesian-type regularity assumption, i.e., each function is a sample from a Gaussian process." @default.
- W3132014543 created "2021-03-01" @default.
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- W3132014543 date "2021-02-11" @default.
- W3132014543 modified "2023-09-24" @default.
- W3132014543 title "No-Regret Algorithms for Time-Varying Bayesian Optimization" @default.
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- W3132014543 doi "https://doi.org/10.48550/arxiv.2102.06296" @default.
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