BO-pro-c-tr reduces regret and overhead in large-scale Bayesian optimization
Achieves 2.6% reduction in simple regret and 34.9% in computational overhead compared to independent local GP models.
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Stories tagged #gaussian-process
Gaussian process bandits proposed for constrained black-box lever optimization
Authors report a statistically significant efficiency gain versus epsilon-greedy in one live production randomized trial.
New acquisition speeds Bayesian optimization on the unit cube
The proposed acquisition is globally solvable faster than multi-start quasi-Newton and matches Gaussian-process accuracy per evaluation on cheaper black boxes.
Sparsifying Stochasticity, Not Capacity: Partial Stochasticity via Deep Weight Factorization of Prior Scales
A Bayesian network method sparsifies stochastic parameters via prior-scale factorization while matching fully stochastic accuracy.
Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models
Paper applies amortized structured stochastic variational inference to Gaussian Process Latent Variable Models, letting latent posteriors depend on inducing points and improving manifold reconstruction metrics.
Context-Continuous Preference Learning for Exoskeleton Personalization
A Gaussian-process preference model shares nearby-context feedback to personalize ankle and elbow exoskeleton assistance with less data.
WhatWorkedBench: Benchmarking Experimental Understanding in AI Agents
WhatWorkedBench tests whether AI agents can predict experiment outcomes across 36 tasks.
Reduced-Space Multi-Fidelity Bayesian Optimization of Process Simulation Models
RS-MFBO couples global sensitivity analysis with fidelity-augmented Gaussian processes to slash costly high-fidelity simulation runs in industrial flowsheet optimization.