[2510.04676] Counterfactual Credit Guided Bayesian Optimization

Computer Science > Machine Learning arXiv:2510.04676 (cs) [Submitted on 6 Oct 2025 (v1), last revised 1 Mar 2026 (this version, v2)] Title:Counterfactual Credit Guided Bayesian Optimization Authors:Qiyu Wei, Haowei Wang, Richard Allmendinger, Mauricio A. Álvarez View a PDF of the paper titled Counterfactual Credit Guided Bayesian Optimization, by Qiyu Wei and 3 other authors View PDF HTML (experimental) Abstract:Bayesian optimization has emerged as a prominent methodology for optimizing expensive black-box functions by leveraging Gaussian process surrogates, which focus on capturing the global characteristics of the objective function. However, in numerous practical scenarios, the primary objective is not to construct an exhaustive global surrogate, but rather to quickly pinpoint the global optimum. Due to the aleatoric nature of the sequential optimization problem and its dependence on the quality of the surrogate model and the initial design, it is restrictive to assume that all observed samples contribute equally to the discovery of the optimum in this context. In this paper, we introduce Counterfactual Credit Guided Bayesian Optimization (CCGBO), a novel framework that explicitly quantifies the contribution of individual historical observations through counterfactual credit. By incorporating counterfactual credit into the acquisition function, our approach can selectively allocate resources in areas where optimal solutions are most likely to occur. We prove that CCGBO ...