High Per Parameter: A Large-Scale Study of Hyperparameter Tuning for Machine Learning Algorithms

13 July 2022

Papers citing "High Per Parameter: A Large-Scale Study of Hyperparameter Tuning for Machine Learning Algorithms"

5 / 5 papers shown

Title
Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020 Ryan Turner David Eriksson M. McCourt J. Kiili Eero Laaksonen Zhen Xu Isabelle M Guyon BDL 60 302 0 20 Apr 2021
PMLB v1.0: An open source dataset collection for benchmarking machine learning methods Joseph D. Romano Trang T. Le William La Cava John T. Gregg Daniel J. Goldberg Natasha L. Ray Praneel Chakraborty Daniel Himmelstein Weixuan Fu J. Moore GP 43 74 0 30 Nov 2020
Importance of Tuning Hyperparameters of Machine Learning Algorithms Hilde J. P. Weerts A. Mueller Joaquin Vanschoren 41 110 0 15 Jul 2020
Optuna: A Next-generation Hyperparameter Optimization Framework Takuya Akiba Shotaro Sano Toshihiko Yanase Takeru Ohta Masanori Koyama 663 5,808 0 25 Jul 2019
Tunability: Importance of Hyperparameters of Machine Learning Algorithms Philipp Probst B. Bischl A. Boulesteix 60 616 0 26 Feb 2018