The paper introduces KENDO, a unified framework that combines Ensemble Gaussian Processes with disagreement‑aware acquisition strategies to address hyperparameter selection in Bayesian optimization and active learning. By replacing costly hyperparameter sampling with a kernel ensemble and adaptive Bayesian weighting, KENDO‑BO and KENDO‑AL provide self‑correcting mechanisms tailored to their respective tasks. Experiments on synthetic and real‑world benchmarks show that KENDO‑BO matches or outperforms state‑of‑the‑art methods while cutting computational cost up to fivefold, and KENDO‑AL delivers better predictive calibration with up to 27‑times speedup compared to MCMC‑based baselines.
By Heng Zhang, Haotian Xiang, Qin Lu, Konstantinos D. Polyzos, Tara Javidi
arXiv:2606. 07561v1 Announce Type: new Abstract: Gaussian processes with stationary kernels on bounded domains exhibit inflated posterior variance near the boundary.
By Maria B{\aa}nkestad, Sanna Jarl, Jens Sj\"olund
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
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:2607. 18282v1 Announce Type: new Abstract: Bayesian Optimization is widely used for expensive black-box optimization, yet its success often depends on choosing a kernel that matches the objective's unknown structure.
By Weibo Huang, Cheng Hua
arXiv:2606. 06984v1 Announce Type: new Abstract: This paper presents a general acceleration mechanism for multi-objective Bayesian optimisation (MOBO) that leverages Gaussian process predictive gradients as auxiliary signals.
By Alma Rahat, Tinkle Chugh, Jonathan Fieldsend, Richard Allmendinger
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: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
arXiv:2606. 26497v1 Announce Type: new Abstract: Bayesian filtering of partially and noisily observed dynamical systems seeks to infer the evolving conditional distribution of the state of a dynamical system, given observations, in an online fashion.
By Eviatar Bach, Ricardo Baptista, Jochen Br\"ocker, Bohan Chen, Andrew Stuart
arXiv:2605. 07565v2 Announce Type: replace-cross Abstract: We study Bayesian Optimisation (BO) in settings where the objective function is influenced by uncontrollable environmental contexts governed by an unknown probability distribution.
By Tigran Ramazyan, Denis Derkach
arXiv:2609.28015v1 Announce Type: cross
Abstract: Data assimilation estimates a dynamical state from partial and noisy observations. Classical ensemble filters are efficient but restrict analysis upd...
By Haoyuan Chen, Alexandre Thi\'ery
arXiv:2605. 08179v2 Announce Type: replace-cross Abstract: Radar sounders are electromagnetic instruments that can probe deep into the subsurface of Earth and other planetary bodies by processing the echo of transmitted radar waves.
By Jordy Dal Corso, Annalena Kofler, Marco Cortellazzi, Lorenzo Bruzzone, Bernhard Sch\"olkopf