Optimum-statistical Collaboration Towards General and Efficient Black-box Optimization

Published: 02 Jun 2023, Last Modified: 02 Jun 2023Accepted by TMLREveryoneRevisionsBibTeX
Abstract: In this paper, we make the key delineation on the roles of resolution and statistical uncertainty in hierarchical bandits-based black-box optimization algorithms, guiding a more general analysis and a more efficient algorithm design. We introduce the optimum-statistical collaboration, an algorithm framework of managing the interaction between optimization error flux and statistical error flux evolving in the optimization process. We provide a general analysis of this framework without specifying the forms of statistical error and uncertainty quantifier. Our framework and its analysis, due to their generality, can be applied to a large family of functions and partitions that satisfy different local smoothness assumptions and have different numbers of local optimums, which is much richer than the class of functions studied in prior works. Our framework also inspires us to propose a better measure of the statistical uncertainty and consequently a variance-adaptive algorithm VHCT. In theory, we prove the algorithm enjoys rate-optimal regret bounds under different local smoothness assumptions; in experiments, we show the algorithm outperforms prior efforts in different settings.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Roman_Garnett1
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Submission Number: 774