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Despite all the benefits of automated hyperparameter optimization (HPO), most modern HPO algorithms are black-boxes themselves.
On a measure of the information provided by an experiment
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Efficient global optimization of expensive black-box functions
Donald R. Jones, Matthias Schonlau, and William J. Welch · 1998
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Greedy function approximation: a gradient boosting machine
Jerome H Friedman · 2001
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A taxonomy of global optimization methods based on response surfaces
Donald R. Jones · 2001
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Statistical comparisons of classifiers over multiple data sets
Janez Demsar · 2006
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Bagging and boosting classification trees to predict churn
Aurélie Lemmens and Christophe Croux · 2006
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Random forests for classification in ecology
D Richard Cutler, Thomas C Edwards Jr, Karen H Beard, Adele Cutler, Kyle T Hess, Jacob Gibson, and Joshua J Lawler · 2007
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Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power
Salvador García, Alberto Fernández, Julián Luengo, and Francisco Herrera · 2010
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Gaussian process optimization in the bandit setting: No regret and experimental design
Niranjan Srinivas, Andreas Krause, Sham M. Kakade, and Matthias W. Seeger · 2010
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Algorithms for hyper-parameter optimization
James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 2011
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Sequential model-based optimization for general algorithm configuration
Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown · 2011
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Entropy search for information-efficient global optimization
Philipp Hennig and Christian J. Schuler · 2012
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Practical Bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P. Adams · 2012
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Assessing transferability of ecological models: an underappreciated aspect of statistical validation
Seth J Wenger and Julian D Olden · 2012
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Auto-weka: combined selection and hyperparameter optimization of classification algorithms
Chris Thornton, Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown · 2013
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Predictive entropy search for efficient global optimization of black-box functions
José Miguel Hernández-Lobato, Matthew W. Hoffman, and Zoubin Ghahramani · 2014
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An efficient approach for assessing hyperparameter importance
Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown · 2014
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TPOT: A tree-based pipeline optimization tool for automating machine learning
Randal S. Olson and Jason H. Moore · 2016
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Efficient parameter importance analysis via ablation with surrogates
Andre Biedenkapp, Marius Lindauer, Katharina Eggensperger, Frank Hutter, Chris Fawcett, and Holger H. Hoos · 2017
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Google vizier: A service for black-box optimization
Daniel Golovin, Benjamin Solnik, Subhodeep Moitra, Greg Kochanski, John Karro, and D. Sculley · 2017
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Hyperband: A novel bandit-based approach to hyperparameter optimization
Lisha Li, Kevin G. Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar · 2017
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GPflow: A Gaussian process library using TensorFlow
Multi-objective hyperparameter tuning and feature selection using filter ensembles
Martin Binder, Julia Moosbauer, Janek Thomas, and Bernd Bischl · 2020
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Trust in AutoML
Jaimie Drozdal, Justin Weisz, Dakuo Wang, Gaurav Dass, Bingsheng Yao, Changruo Zhao, Michael Muller, Lin Ju, and Hui Su · 2020
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Bayesian optimization is superior to random search for machine learning hyperparameter tuning: Analysis of the black-box optimization challenge 2020
Ryan Turner, David Eriksson, Michael McCourt, Juha Kiili, Eero Laaksonen, Zhen Xu, and Isabelle Guyon · 2020
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Putting the human back in the AutoML loop
Iordanis Xanthopoulos, Ioannis Tsamardinos, Vassilis Christophides, Eric Simon, and Alejandro Salinger · 2020
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Hyperparameter optimization: Foundations, algorithms, best practices and open challenges
Bernd Bischl, Martin Binder, Michel Lang, Tobias Pielok, Jakob Richter, Stefan Coors, Janek Thomas, Theresa Ullmann, Marc Becker, Anne-Laure Boulesteix, Difan Deng, and Marius Lindauer · 2021
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Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke. Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani, and James Hensman · 2017
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Bayesian optimization with gradients
Jian Wu, Matthias Poloczek, Andrew Gordon Wilson, and Peter I. Frazier · 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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Hyperparameter importance across datasets
Jan N Van Rijn and Frank Hutter · 2018
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Hyperspace: Distributed bayesian hyperparameter optimization
M. Todd Young, Jacob D. Hinkle, Arvind Ramanathan, and Ramakrishnan Kannan · 2018
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Towards automated deep learning: Efficient joint neural architecture and hyperparameter search
Arber Zela, Aaron Klein, Stefan Falkner, and Frank Hutter · 2018
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Opening the black box of neural networks: methods for interpreting neural network models in clinical applications
Zhongheng Zhang, Marcus W Beck, David A Winkler, Bin Huang, Wilbert Sibanda, Hemant Goyal, et al · 2018
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Learning interpretable models through multi-objective neural architecture search
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Automatic componentwise boosting: An interpretable automl system
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Explaining hyperparameter optimization via partial dependence plots
Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, and Bernd Bischl · 2021
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Bayesian algorithm execution: Estimating computable properties of black-box functions using mutual information
Willie Neiswanger, Ke Alexander Wang, and Stefano Ermon · 2021
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YAHPO gym - design criteria and a new multifidelity benchmark for hyperparameter optimization
Florian Pfisterer, Lennart Schneider, Julia Moosbauer, Martin Binder, and Bernd Bischl · 2021
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Auto-PyTorch Tabular: Multi-fidelity metalearning for efficient and robust AutoDL
Lucas Zimmer, Marius Lindauer, and Frank Hutter · 2021
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Why do machine learning practitioners still use manual tuning? A qualitative study
Niklas Hasebrook, Felix Morsbach, Niclas Kannengießer, Jörg K. H. Franke, Frank Hutter, and Ali Sunyaev · 2022
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