Pfns4bo: In-context learning for bayesian optimization

S Müller, M Feurer, N Hollmann… - … on Machine Learning, 2023 - proceedings.mlr.press
In this paper, we use Prior-data Fitted Networks (PFNs) as a flexible surrogate for Bayesian
Optimization (BO). PFNs are neural processes that are trained to approximate the posterior …

[HTML][HTML] A comparative study of infill sampling criteria for computationally expensive constrained optimization problems

K Chaiyotha, T Krityakierne - Symmetry, 2020 - mdpi.com
Engineering optimization problems often involve computationally expensive black-box
simulations of underlying physical phenomena. This paper compares the performance of …

Using past experience for configuration of Gaussian processes in Black-Box Optimization

J Koza, J Tumpach, Z Pitra, M Holeňa - International Conference on …, 2021 - Springer
This paper deals with the configuration of Gaussian processes serving as surrogate models
in black-box optimization. It examines several different covariance functions of Gaussian …

Robust and optimal design of railway vehicle system for derailment risk using efficient global optimisation method

YC Cheng, CK Lee, CL Hsieh - International Journal of …, 2023 - inderscienceonline.com
This paper presents an innovative optimisation procedure, combining uniform design (UD)
and the efficient global optimisation (EGO) algorithm, to generate a set of robust suspension …