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Large-Scale Bayesian Optimisation Using Uncertainty-Calibrated Product-of-Experts Gaussian Process Models with Trust Regions

Paper recorded by Signals 4 on 2026-11-01 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-11-01 on arXiv · recorded by Signals 4 on 2026-11-03

Category: cs.LG · 机器学习 · first seen 2026-11-03

Abstract

The use of collections of independent local Gaussian processes (GP-ind) combined with trust regions is a widely adopted approach for large-scale and high-dimensional Bayesian optimisation (BO). However, this approach suffers from several limitations, including discontinuities at the boundaries of disjoint regions, an inability to capture long-range spatial correlations, potential information loss,

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#31 most recent of 703 cs.LG papers we have recorded · ↑ newer: Seeing Why: Exact Evidence Maps for Quantum and Optical Image Classifi · ↓ older: OC-OPD: Observation-coordinated Supervision for On-Policy Distillation
Cite this page: Large-Scale Bayesian Optimisation Using Uncertainty-Calibrated Product-of-Experts Gaussian Process Models with Trust Regions: the #31 most recent of 703 cs.LG papers we have recorded (as of 2026-11-01). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/large-scale-bayesian-optimisation-using-uncertainty-calibrated-product-of-expert.html
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