Get Started
Research questionHow should Bayesian optimization allocate replications for expensive, high-variance stochastic objectives?High-variance objective observations can make a single evaluation unreliable, while obtaining enough repeated samples to estimate performance is expensive. The optimization process must therefore decide where additional evaluations are most valuable without wasting the simulation budget.
Machine Learning
Research Paper
Statistical Machine Learning
Latest papersRecent research connected to this question, newest first.Adaptive Replication Strategies in Trust-Region-Based Bayesian Optimization of Stochastic FunctionsThe source studies Gaussian-process Bayesian optimization within a trust-region framework, using adaptive replication, acquisition-function modifications, and cost-aware evaluation strategies. Its numerical experiments examine whether these choices improve computational efficiency while preserving solution accuracy relative to baseline methods, particularly when many samples are needed to reduce noise.research paper · Sep 2, 2026
Related questions
How can Bayesian optimization respect permutation symmetry in unordered groups of decision variables?How can self-driving laboratories adapt long-running Bayesian optimization campaigns as scientific goals and execution conditions change?How can we estimate simulator-parameter distributions that reproduce observations without inaccurate fixed likelihood surrogates?How can SGMCMC hyperparameters be tuned without Metropolis-Hastings acceptance rates when computation is limited?
Home
Topics
Search
Library