The paper introduces Finite-Library Input-Warped Bayesian Optimization (FLIWBO), a method that selects input warps from a finite library to adapt the geometry used by Gaussian‑process Bayesian optimization. FLIWBO maintains high‑probability convergence guarantees while improving sample efficiency on problems where raw coordinates poorly match the objective’s geometry, such as log‑scaled hyperparameters or localized peaks. Experiments on synthetic benchmarks, Fashion‑MNIST hyperparameter tuning, and a 20‑dimensional multi‑agent system design demonstrate that FLIWBO‑UCB outperforms raw‑coordinate GP‑UCB and other methods with regret guarantees, especially under misspecified geometry.
arXiv:2601. 07094v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) iteratively fits a Gaussian process (GP) surrogate to accumulated evaluations and selects new queries via an acquisition function.
tidyHEBO is a BoTorch-native Bayesian optimization tool that jointly applies Yeo-Johnson output warping to a Gaussian‑process surrogate, evaluates acquisition functions on the original objective scale, and conducts constrained cumulative Pareto search across multiple acquisition criteria. Using only default settings, it outperformed other methods on the Olympus benchmark and performed strongly on synthetic, Needle‑in‑a‑Haystack, and Bayesmark tasks, while adaptive batching offered a trade‑off between parallelization and optimization quality. These results position tidyHEBO as a robust, reproducible optimizer suitable for diverse practical problems, including scientific applications and hyperparameter tuning.
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...
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...
LLINBO is a framework that integrates Large Language Models (LLMs) into Bayesian Optimization (BO) by deciding when to use LLM suggestions and when to fall back on classical BO. It introduces three interaction mechanisms, each with a no‑harm theoretical guarantee of sublinear cumulative regret for common kernels, without requiring performance assumptions on the LLM. Experiments show that LLINBO improves the reliability of LLM‑based BO and outperforms classical BO in both simulations and a real‑world 3D printing task.
arXiv:2602.07144v3 Announce Type: replace Abstract: Bayesian optimization (BO) is a popular technique for sample-efficient optimization of black-box functions. In many applications, the parameters be...
arXiv:2605. 20145v2 Announce Type: replace-cross Abstract: Gaussian process (GP) predictive distributions are commonly used in Bayesian optimization (BO) to guide the selection of evaluation points for expensive objective functions.
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:2610.08969v1 Announce Type: new Abstract: Black-box optimization problems are ubiquitous across science and engineering, often dealing with expensive objective functions. This objective latency...
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:2608. 13793v1 Announce Type: cross Abstract: Machine learning (ML) has become an indispensable part of modern engineering design workflows.
arXiv:2607.10669v3 Announce Type: replace Abstract: Bayesian optimization is widely used for expensive black-box optimization problems. While methodological advances often focus on individual compone...