Surrogate Modeling for Bayesian Optimization beyond a Single Gaussian Process

Qin Lu, Konstantinos Polyzos, Bingcong Li, Georgios B. Giannakis

    Research output: Contribution to journalArticlepeer-review

    3 Scopus citations


    Bayesian optimization (BO) has well-documented merits for optimizing black-box functions with an expensive evaluation cost. Such functions emerge in applications as diverse as hyperparameter tuning, drug discovery, and robotics. BO hinges on a Bayesian surrogate model to sequentially select query points so as to balance exploration with exploitation of the search space. Most existing works rely on a single Gaussian process (GP) based surrogate model, where the kernel function form is typically preselected using domain knowledge. To bypass such a design process, this paper leverages an ensemble (E) of GPs to adaptively select the surrogate model fit on-the-fly, yielding a GP mixture posterior with enhanced expressiveness for the sought function. Acquisition of the next evaluation input using this EGP-based function posterior is then enabled by Thompson sampling (TS) that requires no additional design parameters. To endow function sampling with scalability, random feature-based kernel approximation is leveraged per GP model. The novel EGP-TS readily accommodates parallel operation. To further establish convergence of the proposed EGP-TS to the global optimum, analysis is conducted based on the notion of Bayesian regret for both sequential and parallel settings. Tests on synthetic functions and real-world applications showcase the merits of the proposed method.

    Original languageEnglish (US)
    Pages (from-to)11283-11296
    Number of pages14
    JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
    Issue number9
    StatePublished - Sep 1 2023

    Bibliographical note

    Publisher Copyright:
    © 1979-2012 IEEE.


    • Bayesian optimization
    • Bayesian regret analysis
    • Gaussian processes
    • Thompson sampling
    • ensemble learning

    PubMed: MeSH publication types

    • Journal Article


    Dive into the research topics of 'Surrogate Modeling for Bayesian Optimization beyond a Single Gaussian Process'. Together they form a unique fingerprint.

    Cite this