arXiv Machine Learning
Principled Design of Expected Improvement for Continuous Simulation Optimization
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
1d ago
Adaptive Conditional Residual Sampling for Spectral Risk Optimisation under Decision-Dependent Uncertainty
arXiv:2603.12507v3 Announce Type: replace Abstract: We consider minimising a spectral risk objective, the expected cost plus a weighted conditional value-at-risk, when the distribution of the uncerta...
arXiv Machine Learning
Aug 4
Robust Bayesian Optimization via Tempered Posteriors
arXiv:2601. 07094v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) iteratively fits a Gaussian process (GP) surrogate to accumulated evaluations and selects new queries via an acquisition function.
arXiv Statistics ML
Sep 11
Learning-Based Surrogate Method for Stochastic Optimization under Decision-Dependent Uncertainty with Adaptive Random Designs
The paper introduces a learning-based surrogate approach for stochastic optimization problems where uncertainty depends on the decision, modeled via a nonparametric regression. It constructs a surrogate that embeds iteratively updated Jacobian estimates, using an adaptive random design that focuses sampling near the current iterate to achieve dimension‑independent convergence of the Jacobian estimates. The resulting learning‑based stochastic prox‑linear (L‑SPL) algorithm demonstrates nonasymptotic convergence rates and outperforms existing methods in sample efficiency and objective value in numerical experiments.
arXiv Statistics ML
Sep 3
Adaptive Replication Strategies in Trust-Region-Based Bayesian Optimization of Stochastic Functions
The paper presents OGPIT, a trust‑region Bayesian optimization method that uses Gaussian process models and adaptive replication to handle stochastic functions with high variance. By allocating repeated evaluations where most beneficial and incorporating cost‑aware acquisition modifications, the approach scales efficiently when many samples are needed to reduce noise. Numerical experiments demonstrate that adaptive replication improves computational efficiency while maintaining solution accuracy compared to baseline methods.
arXiv Machine Learning
Sep 3
HyperMC: Multi-Fidelity Hyperparameter Tuning for Stochastic Gradient MCMC
HyperMC is a multi‑fidelity hyperparameter tuning framework for stochastic gradient Markov chain Monte Carlo (SGMCMC) that combines Hyperband-style resource allocation with kernel Stein discrepancy (KSD) evaluation. It uses successive‑halving brackets to explore a continuous hyperparameter space while progressively refining promising configurations within a fixed computational budget. Robust HyperMC further introduces global grid initialization and elite‑guided local refinement to reduce sensitivity to random candidate generation and noisy evaluations, and theoretical analysis shows that the successive‑halving component selects a near‑optimal configuration with high probability under suitable conditions.
arXiv Machine Learning
5d ago
Convergence Rates of Gaussian Process Thompson Sampling for Efficient Global Optimization
arXiv:2610.19925v1 Announce Type: cross Abstract: Efficient global optimization aims to identify a global optimum of an expensive black-box function within a limited evaluation budget $N$. We investi...