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arXiv Statistics ML

Active Pilot Sampling for Multi-fidelity Estimation in Stochastic Optimization

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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.

By Boyang Shen, Junyi Liu
ragbenchmarks
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arXiv Machine Learning
Oct 23

SABLE: Scalable Affine Bayesian Latent-space Exploration

SABLE is a Bayesian optimisation method designed for high‑dimensional latent spaces where the latent dimension far exceeds the evaluation budget. It uses a Bayesian affine surrogate that decomposes each query into an in‑span component optimized by expected improvement and an off‑span component governed by a scalar norm, with a finite‑horizon schedule to avoid under‑exploration. A recompression scheme reduces the number of pseudo‑observations to ≠O(M), keeping refitting costs manageable while achieving high sample efficiency and rapid decision times on synthetic and latent‑space optimisation tasks.

By Erik Bodin, Lisa Maria Kreusser, Donney Fan, Henry Moss, Geoff Pleiss, Nicolas Durrande
benchmarks
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arXiv Machine Learning
Oct 9

Exploiting Gradients in Bayesian Inference of Expensive Simulators

The paper explores how to use gradients of simulator outputs with respect to input parameters to improve Bayesian inference for expensive simulators. By incorporating gradient information into Gaussian process surrogate models, the authors show that Bayesian optimization can converge faster under a limited simulation budget. They find that reverse‑mode differentiation yields efficiency gains, while forward‑mode does not offset its computational cost, especially when the number of parameters exceeds the output dimensionality.

By \v{S}imon Sold\'at, V\'aclav \v{S}m\'idl
efficiency
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arXiv Machine Learning
Jul 9

Optimization-Embedded Active Multi-Fidelity Surrogate Learning for Multi-Condition Airfoil Shape Optimization

arXiv:2603. 17057v2 Announce Type: replace-cross Abstract: Active multi-fidelity surrogate modeling is developed for multi-condition airfoil shape optimization to reduce high-fidelity CFD cost while retaining RANS-consistent aerodynamic metrics.

By Isaac Robledo, Alberto Vilari\~no, Arnau Mir\'o, Oriol Lehmkuhl, Carlos Sanmiguel Vila, Rodrigo Castellanos
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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.

By Ming Tan, Xiyun Jiao
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