Learning search spaces for Bayesian optimization: Another view of
hyperparameter transfer learning

Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning

27 September 2019

Valerio Perrone

Matthias Seeger

Cédric Archambeau

Rodolphe Jenatton

Papers citing "Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning"

18 / 18 papers shown

Title
Sequential Large Language Model-Based Hyper-parameter Optimization Kanan Mahammadli Seyda Ertekin 45 4 0 27 Oct 2024
Data-driven Prior Learning for Bayesian Optimisation Sigrid Passano Hellan Christopher G. Lucas Nigel H. Goddard 26 0 0 24 Nov 2023
PED-ANOVA: Efficiently Quantifying Hyperparameter Importance in Arbitrary Subspaces Shuhei Watanabe Archit Bansal Frank Hutter 32 12 0 20 Apr 2023
Transfer Learning for Bayesian Optimization: A Survey Tianyi Bai Yang Li Yu Shen Xinyi Zhang Wentao Zhang Bin Cui BDL 37 29 0 12 Feb 2023
Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-Structured Parzen Estimator Shuhei Watanabe Noor H. Awad Masaki Onishi Frank Hutter 29 8 0 13 Dec 2022
HPC Storage Service Autotuning Using Variational-Autoencoder-Guided Asynchronous Bayesian Optimization Matthieu Dorier Romain Egele Prasanna Balaprakash Jaehoon Koo Sandeep Madireddy Srinivasan Ramesh A. Malony R. Ross 14 9 0 03 Oct 2022
TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning Yang Li Yu Shen Huaijun Jiang Wentao Zhang Zhi-Xin Yang Ce Zhang Bin Cui 20 15 0 06 Jun 2022
Transfer Learning based Search Space Design for Hyperparameter Tuning Yang Li Yu Shen Huaijun Jiang Tianyi Bai Wentao Zhang Ce Zhang Bin Cui 30 13 0 06 Jun 2022
ODBO: Bayesian Optimization with Search Space Prescreening for Directed Protein Evolution Lixue Cheng Ziyi Yang Chang-Yu Hsieh Ben Liao Shengyu Zhang 25 6 0 19 May 2022
Positive-Unlabeled Domain Adaptation Jonas Sonntag Gunnar Behrens Lars Schmidt-Thieme 8 2 0 11 Feb 2022
FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning Yi Zhou Parikshit Ram Theodoros Salonidis Nathalie Baracaldo Horst Samulowitz Heiko Ludwig AI4CE 24 25 0 15 Dec 2021
Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation Ross M. Clarke E. T. Oldewage José Miguel Hernández-Lobato 20 9 0 20 Oct 2021
A multi-objective perspective on jointly tuning hardware and hyperparameters David Salinas Valerio Perrone Olivier Cruchant Cédric Archambeau 24 13 0 10 Jun 2021
JUMBO: Scalable Multi-task Bayesian Optimization using Offline Data Kourosh Hakhamaneshi Pieter Abbeel Vladimir M. Stojanović Aditya Grover 19 10 0 02 Jun 2021
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization Valerio Perrone Huibin Shen Aida Zolic I. Shcherbatyi Amr Ahmed ... Barbara Pogorzelska Miroslav Miladinovic K. Kenthapadi Matthias Seeger Cédric Archambeau 13 16 0 15 Dec 2020
Resonance: Replacing Software Constants with Context-Aware Models in Real-time Communication J. Gupchup A. Aazami Yaran Fan Senja Filipi Tom Finley ... D. Perednya Sriram Srinivasan John Langford Ross Cutler J. Gehrke OffRL 19 1 0 23 Nov 2020
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter Optimization Yimin Huang Yujun Li Hanrong Ye Zhenguo Li Zhihua Zhang 22 7 0 11 Jul 2020
Practical Transfer Learning for Bayesian Optimization Matthias Feurer Benjamin Letham Frank Hutter E. Bakshy 45 34 0 06 Feb 2018