arXiv Machine Learning July 27, 2026 By Hirotaka Sugawara, Yujin Taguchi, Kei Minagawa, Yusuke Hiki, Takashi Morikura, Akira Funahashi

Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression

Read the original on arXiv Machine Learning →

arXiv:2607. 22238v1 Announce Type: new Abstract: Bayesian optimization (BO) is an optimization method that sequentially proposes the next candidate explainable variables for optimizing target variables by balancing exploration and exploitation.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization

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arXiv Machine Learning

Jun 9

Large-scale empirical tuning and comparison of default optimizers for variational inference

arXiv:2606. 07841v1 Announce Type: cross Abstract: Black-box variational inference (BBVI) is a methodology for posterior approximation that relies on stochastic optimization.

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Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery

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