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#bayesian-optimization

4 curated events
papersTODAY 04:00 UTC

arXiv Paper Decouples Convergence and Diversity in Multi-Objective Bayesian Optimization

A new arXiv preprint introduces a method for multi-objective Bayesian optimization that treats the search for convergence toward the Pareto front and the search for diverse coverage separately. The approach aims to improve how well expensive black-box problems with multiple objectives are approximated under tight sample budgets. The work is theoretical and benchmark-oriented, with no released product or model attached.

research/bayesian-optimization/arxiv/benchmarks
papersTODAY 04:00 UTC

Human-in-the-Loop Meta Bayesian Optimization for Fusion Energy

A new arXiv paper presents a human-in-the-loop meta Bayesian optimization framework aimed at experiments where each trial is costly and scarce, such as inertial confinement fusion. The approach combines learned meta-level priors with human feedback to guide the search over experimental parameters under tight budget constraints. The authors frame the method as applicable to scientific domains beyond fusion that face similar cost and access limits.

bayesian-optimization/fusion-energy/human-in-the-loop/research
papersTODAY 04:00 UTC

Bayesian optimization with kernel ensembles for acoustic source localization

A new arXiv preprint proposes using Bayesian optimization to jointly estimate source location and seabed geoacoustic parameters, a task that normally demands many evaluations of an costly normal-mode propagation model. Instead of a single Gaussian process surrogate, the method combines an ensemble of kernels and selects the next sampling point based on disagreement among them. The authors report accurate parameter estimates while keeping the number of expensive model runs low.

research/bayesian-optimization/acoustics/gaussian-processes
papersSEP 11 04:00 UTC

arXiv paper targets lower-tail calibration of Gaussian processes for Bayesian optimization

An updated arXiv preprint proposes a goal-oriented approach to calibrating the lower tail of Gaussian process predictive distributions, which Bayesian optimization uses to choose where to evaluate costly objective functions. The abstract notes that kernel and hyperparameter choices strongly shape these predictions. The submission is a replacement version (v2) of an earlier preprint.

bayesian-optimization/gaussian-processes/calibration/arxiv