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Zero-shot hyperparameter optimization (HPO) is a simple yet effective use of transfer learning for constructing a small list of hyperparameter (HP) configurations that complement each other.
An analysis of approximations for maximizing submodular set functions—i
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The nonstochastic multiarmed bandit problem
Peter Auer, Nicolo Cesa-Bianchi, Yoav Freund, and Robert E Schapire · 2002
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Ranking learning algorithms: Using ibl and meta-learning on accuracy and time results
Pavel B Brazdil, Carlos Soares, and Joaquim Pinto Da Costa · 2003
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Paramils: an automatic algorithm configuration framework
Frank Hutter, Holger H Hoos, Kevin Leyton-Brown, and Thomas Stützle · 2009
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An online algorithm for maximizing submodular functions
Matthew Streeter and Daniel Golovin · 2009
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Sequential model-based optimization for general algorithm configuration (extended version)
Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown · 2010
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
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Almost optimal exploration in multi-armed bandits
Zohar Karnin, Tomer Koren, and Oren Somekh · 2013
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Openml: Networked science in machine learning
Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, and Luis Torgo · 2013
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Efficient transfer learning method for automatic hyperparameter tuning
Dani Yogatama and Gideon Mann · 2014
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Initializing bayesian hyperparameter optimization via meta-learning
Matthias Feurer, Jost Tobias Springenberg, and Frank Hutter · 2015
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Learning hyperparameter optimization initializations
Martin Wistuba, Nicolas Schilling, and Lars Schmidt-Thieme · 2015
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Sequential model-free hyperparameter tuning
Martin Wistuba, Nicolas Schilling, and Lars Schmidt-Thieme · 2015
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Tianqi Chen and Carlos Guestrin · 2016
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Non-stochastic best arm identification and hyperparameter optimization
Kevin Jamieson and Ameet Talwalkar · 2016
Analysis of the automl challenge series 2015-2018
Isabelle Guyon, Lisheng Sun-Hosoya, Marc Boullé, Hugo Jair Escalante, Sergio Escalera, Zhengying Liu, Damir Jajetic, Bisakha Ray, Mehreen Saeed, Michéle Sebag, Alexander Statnikov, WeiWei Tu, and Evelyne Viegas · 2018
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Massively parallel hyperparameter tuning
Liam Li, Kevin Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Moritz Hardt, Benjamin Recht, and Ameet Talwalkar · 2018
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Warmstarting of model-based algorithm configuration
Marius Lindauer and Frank Hutter · 2018
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Scalable hyperparameter transfer learning
Valerio Perrone, Rodolphe Jenatton, Matthias W Seeger, and Cédric Archambeau · 2018
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Learning multiple defaults for machine learning algorithms
Florian Pfisterer, Jan N van Rijn, Philipp Probst, Andreas Müller, and Bernd Bischl · 2018
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Lightgbm: A highly efficient gradient boosting decision tree
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu · 2017
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Hyperband: A novel bandit-based approach to hyperparameter optimization
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar · 2017
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Bohb: Robust and efficient hyperparameter optimization at scale
Stefan Falkner, Aaron Klein, and Frank Hutter · 2018
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Catboost: unbiased boosting with categorical features
Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin · 2018
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Meta learning for defaults–symbolic defaults
Jan N van Rijn, Florian Pfisterer, Janek Thomas, Andreas Muller, Bernd Bischl, and Joaquin Vanschoren · 2018
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Automl: methods, systems, challenges (2018)
F Hutter, L Kotthoff, and J Vanschoren · 2019
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Learning search spaces for bayesian optimization: Another view of hyperparameter transfer learning
Valerio Perrone, Huibin Shen, Matthias W Seeger, Cedric Archambeau, and Rodolphe Jenatton · 2019
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