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bayesian-optimization

topic4 events
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
papersTODAY 04:00 UTC

Bayesian Optimisation Method Combines Expert Gaussian Processes With Calibrated Uncertainty

A new arXiv preprint proposes using a product-of-experts Gaussian process as the surrogate model in Bayesian optimisation, rather than a single global GP. The authors address the cubic scaling cost of standard GP regression with training set size, which restricts its use on larger datasets, and add a calibration step for uncertainty estimates. The work falls in the machine learning methodology category and has not yet been peer reviewed.

bayesian-optimisation/gaussian-processes/uncertainty/arxiv
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