arXiv Machine Learning By Shuhei Sugiura, Ichiro Takeuchi, Shion Takeno

Randomized Kriging Believer for Parallel Bayesian Optimization with Regret Bounds

Read the original on arXiv Machine Learning →

arXiv:2603. 01470v3 Announce Type: replace Abstract: We consider the optimization problem of an expensive-to-evaluate black-box function, in which we can obtain noisy function values in parallel.

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arXiv Machine Learning
Jun 2

Local Preferential Bayesian Optimization

arXiv:2606. 02351v1 Announce Type: new Abstract: Bayesian optimization (BO) is a popular and effective approach for tuning expensive, noisy experiments, but requires the formulation of an explicit objective function.

By Johanna Menn, Miriam Kober, Paul Brunzema, David Stenger, Sebastian Trimpe
arXiv Machine Learning
Sep 25

Time-Varying Bayesian Optimization Without a Metronome

Time‑Varying Bayesian Optimization (TVBO) is a framework for optimizing expensive, noisy black‑box functions that change over time. Existing TVBO algorithms assume observations are taken at a constant frequency, an assumption that becomes unrealistic as Gaussian‑process inference scales with the cube of dataset size. This paper relaxes that assumption, derives the first upper regret bound that incorporates variable sampling frequency, and uses the analysis to give practical guidance on dataset sizes and stale‑data policies. An algorithm (BOLT) that follows these recommendations outperforms the current state‑of‑the‑art TVBO methods on both synthetic and real‑world experiments.

By Anthony Bardou, Patrick Thiran