A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.

DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks

Distributed Asynchronous Hyperparameter Optimization better than HyperOpt. 比HyperOpt更强的分布式异步超参优化库。

Collection of Multi-Fidelity benchmark functions

[NeurIPS 2022] Supervising the Multi-Fidelity Race of Hyperparameter Configurations

A Python-based toolbox of various methods in uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.

Vanishing Viscosity solution predicting Graph Neural Networks and Domain Decomposable Reduced Order Models based on the Discontinuous Galerking method applied to Friedrichs' systems

Multi-fidelity modeling of wind farm wakes based on a novel super-fidelity network

[NeurIPS 2022] Supervising the Multi-Fidelity Race of Hyperparameter Configurations

This repository contains research on multi-fidelity Bayesian optimization, that I have presented on the Physics Days 2022

[NeurIPS 2023] Multi-fidelity hyperparameter optimization with deep power laws that achieves state-of-the-art results across diverse benchmarks.

Code for the paper "Multi-Fidelity Best-Arm Identification" (NeurIPS 2022)

Code for the paper "Optimal Multi-Fidelity Best-Arm Identification" (NeurIPS 2024)