HEBO: An Empirical Study of Assumptions in Bayesian Optimisation

DOI10.1613/jair.1.13643OpenAlexW4285043533WikidataQ114594306 ScholiaQ114594306MaRDI QIDQ5094071

Jun Wang, Zhi Wang, Rasul Tutunov, Alexander I. Cowen-Rivers, Hao Jianye, Ryan-Rhys Griffiths, Jan Peters, Haitham Bou-Ammar, Antoine Grosnit, Wenlong Lyu, Alexandre Max Maraval

Publication date: 2 August 2022

Published in: Journal of Artificial Intelligence Research (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2012.03826

zbMATH Keywords

uncertaintyautomated reasoningBayesian optimizationhyperparameter tuning

Mathematics Subject Classification ID

Artificial intelligence (68Txx)

Related Items (3)

HEBO ⋮ Model-independent reconstruction of growth index via Gaussian process ⋮ AutonoML: Towards an Integrated Framework for Autonomous Machine Learning

Uses Software

• UCI-ml
• Scikit
• TensorFlow
• Spearmint
• Hyperopt
• Adam
• pySOT
• SMAC
• OpenTuner
• BOCK
• Nevergrad
• GPflowOpt
• RoBO
• BOHB
• pymoo
• BoTorch
• Hyperband

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
• On the limited memory BFGS method for large scale optimization
• SAMBA: safe model-based \& active reinforcement learning
• Compositionally-warped Gaussian processes
• A new family of power transformations to improve normality or symmetry
• Nonconvex Robust Optimization for Problems with Constraints
• Distribution-Free Two-Sample Tests for Scale
• Multi-fidelity Gaussian Process Bandit Optimisation

This page was built for publication: HEBO: An Empirical Study of Assumptions in Bayesian Optimisation