arXiv Machine Learning

Hyperellipsoid Density Sampling: Exploitative Sequences to Accelerate High-Dimensional Optimization

arXiv:2511. 07836v4 Announce Type: replace-cross Abstract: The curse of dimensionality remains a persistent challenge in modern optimization problems.

arXiv Machine Learning

Jul 15

Hyperellipsoid Density Sampling: Exploitative Sequences to Accelerate High-Dimensional Numerical Optimization

arXiv:2511. 07836v5 Announce Type: replace-cross Abstract: The curse of dimensionality remains a persistent challenge in modern optimization problems.

By Julian G. Soltes

benchmarks

arXiv Machine Learning

Jun 30

A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel

arXiv:2111. 10722v4 Announce Type: replace-cross Abstract: We propose a novel deterministic sampling method, EVI-MMD, to approximate a target distribution $\rho^*$ by minimizing the kernel discrepancy, also known as the Maximum Mean Discrepancy (MMD).

By Yindong Chen, Yiwei Wang, Lulu Kang, Chun Liu

arXiv AI

Jul 13

LLM-Driven Evolutionary Generation of Multi-Objective Bayesian Optimization Algorithms

arXiv:2607. 08791v1 Announce Type: cross Abstract: Designing effective multi-objective Bayesian optimization (MOBO) algorithms requires balancing many interdependent design choices whose optimal configuration is problem-dependent and typically demands deep expertise.

By Georgios Laskaris, Reuben Brasher, Niki van Stein, Elena Raponi, Thomas B\"ack, Florian Neukart

llms benchmarks

arXiv AI

Jun 10

Learning Where to Simulate: Generative Active Sampling for Online PDE Surrogate Training

arXiv:2606. 09949v1 Announce Type: cross Abstract: Data-driven PDE surrogates are trained with data produced by numerical PDE solvers.

By Pierre Cesar (DATAMOVE), Sofya Dymchenko (DATAMOVE), Abhishek Purandare (DATAMOVE), Bruno Raffin (DATAMOVE)

diffusion

arXiv Machine Learning

Jul 22

Unsupervised Multi-kernel Learning for Automated Algorithm Selection

arXiv:2607. 19031v1 Announce Type: new Abstract: Automated algorithm selection in black-box optimization typically relies on supervised models that map landscape features to algorithm performance labels.

By Yihang Lu, Tome Eftimov, Carola Doerr

benchmarks

arXiv Machine Learning

Jul 27

Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression

arXiv:2607. 22238v1 Announce Type: new Abstract: Bayesian optimization (BO) is an optimization method that sequentially proposes the next candidate explainable variables for optimizing target variables by balancing exploration and exploitation.

By Hirotaka Sugawara, Yujin Taguchi, Kei Minagawa, Yusuke Hiki, Takashi Morikura, Akira Funahashi

benchmarks

arXiv Machine Learning

Jul 28

Transfer Learning Architectures for Scalable Multi-Fidelity Bayesian Optimization

arXiv:2607. 23404v1 Announce Type: new Abstract: Self-driving laboratories increasingly rely on multi-fidelity Bayesian optimization (MFBO) to balance cheap, approximate evaluations against scarce, expensive ones, with a predictive surrogate at its core.

By Jaewook Lee, Ethan Errington, Christian D. Lorenz, Miao Guo

robotics fine-tuning benchmarks

arXiv Machine Learning

Jun 19

Evolutionary Two-Stage Hyperparameter Optimization Strategies for Physics-Informed Neural Networks

arXiv:2606. 20442v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) solve Partial Differential Equations (PDEs) by embedding physical laws into neural network training.

By Fedor Buzaev (HSE University), Dmitry Efremenko (HSE University), Egor Bugaev (HSE University), Andrei Ermakov (HSE University, AXXX), Denis Derkach (HSE University), Daria Pugacheva (HSE University, AXXX), Fedor Ratnikov (HSE University)

rag

arXiv Machine Learning

Jun 4

U-Net-Accelerated Quality-Diversity Optimization for Climate-Adaptive Urban Layouts

arXiv:2606. 04658v1 Announce Type: cross Abstract: Optimizing urban layouts for climate adaptation requires balancing building density with cold-air ventilation.

By Alexander Hagg, Tania Guerrero, Dirk Reith

safety

arXiv Machine Learning

Jul 15

Sample Efficient Generative Optimization for Molecular Design

arXiv:2607. 12488v1 Announce Type: new Abstract: Molecular optimization in drug discovery, materials design, and catalysis requires searching vast chemical spaces under tight evaluation budgets, since high-fidelity oracles and experimental measurements are costly.

By Sarina Kopf, Cristina Nevado, Philippe Schwaller

benchmarks

arXiv Machine Learning

Jul 14

Modernizing HEBO: a robust Bayesian optimization baseline for practical heteroskedastic and non-stationary problems

arXiv:2607. 10669v1 Announce Type: new Abstract: Bayesian optimization is increasingly used to guide data-efficient experimentation in chemistry, materials science, and related laboratory settings, but its practical performance depends strongly on how well surrogate-model assumptions match the geometry and noise structure of the underlying objective.

By L. A. Zhukov, E. V. Shaburova, D. V. Antonets

benchmarks

arXiv Machine Learning

Jul 2

Generative Refinement for Low-Budget Black-Box Optimization

arXiv:2607. 00691v1 Announce Type: new Abstract: Black-box optimization is a fundamental science and engineering tool that makes it possible to optimize objectives without gradient information.

By Edouard R. Dufour, Pascal Fua

Related stories