The paper tackles two shortcomings of Gaussian‑process based active learning: (1) the posterior variance is independent of observed values, reducing sensitivity to data structure, and (2) it over‑inflates variance near domain boundaries, causing excessive edge sampling. The authors propose a reconstruction‑driven design density that warps sampling toward regions where the posterior mean changes rapidly, and a geometric equalizer that corrects boundary bias. Experiments on sixteen synthetic and two real‑data benchmarks show that the equalizer consistently improves function reconstruction, while the warp further enhances performance by concentrating measurements where the target function varies most.
By Sanna Jarl, Jens Sj\"olund, Jonathan J. S. Scragg, Maria B{\aa}nkestad
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.
By Jiguang Li, Hengrui Luo
arXiv:2609.14262v1 Announce Type: new
Abstract: Joint source localization and geoacoustic inversion requires optimizing an objective built from an expensive normal mode propagation model. Bayesian op...
By Heng Zhang, Haotian Xiang, Florian Meyer, Qin Lu
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.
By Edvin Ketabati Augustinsson, Robert A. Bridges
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.
By Aur\'elien Pion, Emmanuel Vazquez
The paper introduces HACK GPs, a method that treats kernel selection for Gaussian Processes as an online learning problem with expert advice. Each candidate kernel is viewed as a GP expert, and a distribution over these experts is updated online using AdaHedge based on a loss that reflects both function fit and task alignment. Two variants—Mixture of Gaussians and categorical sampling—are presented, with theoretical guarantees that the weight concentrates on the best kernel under a loss‑gap condition, and empirical results show robust performance across Bayesian optimization, level set estimation, and Bayesian active learning compared to standard kernels and simple ensembles.
By Kavin Aravindan, Mani Tej Sriram, Gautam Dasarathy, Tejas Bodas
The paper investigates Bayesian optimization using information geometry, deriving a local sensitivity tensor from the Fisher information metric that bounds the gradient of reparameterizable acquisition functions. This framework explains vanishing-gradient issues in high-dimensional settings and unifies heuristics like RAASP and dimension-scaled lengthscales. Leveraging this insight, the authors introduce FITR, a trust‑region BO method that replaces lengthscale scaling with local pullback‑Fisher weights, achieving competitive performance on GP benchmarks and extending naturally to non‑isotropic surrogates.
By Saksham Kiroriwal, Julius Pfrommer, J\"urgen Beyerer
arXiv:2606. 08438v1 Announce Type: cross Abstract: Bayesian optimization (BO) is a widely used approach for black-box optimization that uses a Gaussian process (GP) as a surrogate and guides sequential evaluations via an acquisition function, with the ultimate goal of locating the global optimum $\mathbf{x}^{\star}$.
By Yilin Zheng, Haowei Wang, Szu Hui Ng, Enlu Zhou
arXiv:2603. 09793v2 Announce Type: replace Abstract: Bayesian optimization is a data-efficient technique that has been shown to be extremely powerful to optimize expensive, black-box, and possibly noisy objective functions.
By Federico Pavesi, Antonio Candelieri, No\'emie Jaquier
arXiv:2608.29349v1 Announce Type: new
Abstract: Gaussian process (GP) regression with a single global GP (GP-glo) incurs cubic computational cost, limiting scalability to large datasets. Product-of-e...
By Yean Hoon Ong, Paolo Barucca, Wei Pan, Jun Wang
arXiv:2602. 03901v5 Announce Type: replace Abstract: The pursuit of optimal trade-offs in high-dimensional search spaces under stringent computational constraints poses a fundamental challenge for contemporary multi-objective optimization.
By Rong Fu, Chunlei Meng, Haoyu Zhao, Kun Liu, JiaBao Dou, Youjin Wang, Simon James Fong
The paper introduces a joint contour location (jCL) method for actively learning input configurations that simultaneously achieve specified responses across multiple computer experiments. By employing two distinct acquisition schemes—one for exploration and one for exploitation—along with a decision rule, the approach balances learning across multiple response surfaces and provides a natural stopping criterion when no solution exists. The method is demonstrated with Gaussian processes, multitask GPs, and deep GPs, outperforming existing single-response contour location strategies and optimization-based alternatives.
By Shih-Ni Prim, Kevin R. Quinlan, Paul Hawkins, Jagadeesh Movva, Annie S. Booth