The paper introduces Gradient-based Sample Selection Bayesian Optimization (GSSBO), a method that builds the Gaussian process surrogate on a strategically chosen subset of samples rather than the full dataset. By using gradient information to eliminate redundant points while keeping diversity and representativeness, GSSBO achieves sublinear regret bounds and reduces the cubic computational cost of standard BO. Experiments on synthetic and real-world tasks show that this approach maintains comparable optimization performance while significantly cutting GP fitting time and resource usage.
arXiv:2610.10186v1 Announce Type: new Abstract: Bayesian optimization (BO) is widely used as a standard approach for expensive black-box optimization. However, BO algorithms often involve parameters...
arXiv:2607. 29225v1 Announce Type: new Abstract: Bayesian Optimization (BO) is widely adopted for data-efficient optimization in scientific and engineering applications, yet its computational cost is rarely evaluated alongside optimization performance.
arXiv:2606. 07841v1 Announce Type: cross Abstract: Black-box variational inference (BBVI) is a methodology for posterior approximation that relies on stochastic optimization.
arXiv:2507.06529v2 Announce Type: replace Abstract: Bayesian optimization (BO) is a powerful paradigm for optimizing expensive black-box functions. Traditional BO methods typically rely on separate h...
The paper presents a method that uses Constrained Bayesian Optimization (CBO) to minimize the energy consumption of machine learning models while ensuring their generalization performance stays above a specified threshold. By treating energy usage as the primary objective and performance as a constraint, the authors demonstrate that CBO can reduce training energy costs on both regression and classification tasks without sacrificing predictive accuracy.
arXiv:2502. 01226v4 Announce Type: replace Abstract: Gaussian process (GP) bandits provide a powerful framework for performing blackbox optimization of unknown functions.
arXiv:2607. 23404v1 Announce Type: new Abstract: Self-driving laboratories increasingly rely on multi-fidelity Bayesian optimization (MFBO) to balance cheap, approximate evaluations against scarce, expensive ones, with a predictive surrogate at its core.
arXiv:2606. 30228v1 Announce Type: new Abstract: Modern engineering workflows increasingly rely on massive parallel simulation, driving the need for scalable, large-batch Bayesian Optimization (BO).
The paper introduces BO-pro-c, a Bayesian optimisation algorithm that employs a product-of-experts Gaussian process (GP-pro-c) as its surrogate model. GP-pro-c combines multiple local GP experts to improve uncertainty quantification, reduce computational cost, and preserve global correlations, addressing the cubic complexity of single global GP models. Experiments show that BO-pro-c achieves competitive optimisation performance with a 0.9% lower simple regret and a 39.4% reduction in computational overhead compared to a single‑global‑GP baseline.
The paper investigates nonlinear dimensionality reduction for Bayesian optimisation (BO) by transforming high‑dimensional black‑box optimisation problems into a sequence of low‑dimensional latent‑space BO (LSBO) tasks. It extends earlier linear embedding approaches by using variational autoencoders (VAEs), deep metric loss, and adaptive retraining to better capture nonlinear structure, and couples LSBO with sequential domain reduction (SDR‑LSBO) to progressively narrow search domains. Experiments on GPU‑accelerated BoTorch with Matérn‑5/2 Gaussian‑process surrogates show that VAE‑based LSBO outperforms adaptive linear embeddings, and the authors provide a theoretical analysis of latent‑space error versus representation gap under PAC‑Bayes conditions.
MF-SCBO is a new multi‑fidelity extension of Scalable Constrained Bayesian Optimization designed for high‑dimensional black‑box functions with black‑box constraints. It handles an arbitrary number of fidelity levels and non‑nested sampling, addressing gaps in existing methods. Experiments on standard benchmarks and challenging problems show that MF‑SCBO generally converges faster than both single‑fidelity SCBO and other multi‑fidelity approaches in high‑dimensional constrained settings.
arXiv:2608. 04113v1 Announce Type: cross Abstract: Black-box optimization is a ubiquitous problem in science and engineering, often dealing with expensive objective functions with cheaper lower-fidelity proxies available.