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Award ID contains: 1815559

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  1. ; ; (, IEEE Transactions on Signal Processing)
    Full Text Available 
  2. ; ; (, Advances in Neural Information Processing Systems 34 (NeurIPS 2021))
    We consider sequential optimization of an unknown function in a reproducing kernel Hilbert space. We propose a Gaussian process-based algorithm and establish its order-optimal regret performance (up to a poly-logarithmic factor). This is the first GP-based algorithm with an order-optimal regret guarantee. The proposed algorithm is rooted in the methodology of domain shrinking realized through a sequence of tree-based region pruning and refining to concentrate queries in increasingly smaller high-performing regions of the function domain. The search for high-performing regions is localized and guided by an iterative estimation of the optimal function value to ensure both learning efficiency and computational efficiency. 
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    Accepted Manuscript Full Text Available
  3. ; ; (, IEEE Transactions on Information Theory)
    null (Ed.)
    Full Text Available 
  4. ; (, IEEE Transactions on Signal Processing)
    null (Ed.)
    Full Text Available 
  5. ; ; (, IEEE Signal Processing Letters)
    Full Text Available 
  6. ; (, IEEE Signal Processing Magazine)
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  7. ; ; ; (, IEEE Transactions on Signal Processing)
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  8. ; ; (, IEEE 58th Conference on Decision and Control (CDC))
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  9. ; ; (, 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton))
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  10. ; ; (, IEEE Transactions on Signal Processing)
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