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Hyperparameter optimization (HPO) is a powerful technique for automating the tuning of machine learning (ML) models.
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Nidamarthi Srinivas and Kalyanmoy Deb · 1994
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Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid
Ron Kohavi et al · 1996
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Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces
R. Storn and K. Price · 1997
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Multiobjective optimization using evolutionary algorithms — a comparative case study
E. Zitzler and L. Thiele · 1998
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A fast and elitist multiobjective genetic algorithm: Nsga-ii
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan · 2002
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A fast and elitist multiobjective genetic algorithm: Nsga-ii
Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and TAMT Meyarivan · 2002
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Bounded archiving using the lebesgue measure
Joshua D Knowles, David W Corne, and Mark Fleischer · 2003
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An emo algorithm using the hypervolume measure as selection criterion
M. Emmerich, N. Beume, and B. Naujoks · 2005
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Efficient global optimization (ego) for multi-objective problem and data mining
S. Jeong and S. Obayashi · 2005
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A summary-attainment-surface plotting method for visualizing the performance of stochastic multiobjective optimizers
Joshua Knowles · 2005
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ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems
J. D. Knowles · 2006
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Sms-emoa: Multiobjective selection based on dominated hypervolume
Nicola Beume, Boris Naujoks, and Michael Emmerich · 2007
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Moea/d: A multiobjective evolutionary algorithm based on decomposition
Q. Zhang and H. Li · 2007
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Moea/d: A multiobjective evolutionary algorithm based on decomposition
Qingfu Zhang and Hui Li · 2007
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Multiobjective optimization on a limited budget of evaluations using model-assisted 𝒮 \mathcal{S} -Metric selection
W. Ponweiser, T. Wagner, D. Biermann, and M. Vincze · 2008
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Algorithms for hyper-parameter optimization
J. Bergstra, R. Bardenet, Y. Bengio, and B. Kégl · 2011
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Predictive entropy search for multi-objective bayesian optimization
D. Hernández-Lobato, J. Hernández-Lobato, A. Shah, and R. Adams · 2016
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Non-stochastic best arm identification and Hyperparameter Optimization
K. Jamieson and A. Talwalkar · 2016
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Algorithmic decision making and the cost of fairness
S. Corbett-Davies, E. Pierson, A. Feller, S. Goel, and A. Huq · 2017
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Hyperband: Bandit-based configuration evaluation for Hyperparameter Optimization
L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, and A. Talwalkar · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Multi-objective multi-fidelity hyperparameter optimization with application to fairness
R. Schmucker, M. Donini, V. Perrone, and C. Archambeau · 2020
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Nas-bench-1shot1: Benchmarking and dissecting one-shot neural architecture search
Arber Zela, Julien Siems, and Frank Hutter · 2020
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DEHB: Evolutionary hyberband for scalable, robust and efficient Hyperparameter Optimization
N. Awad, N. Mallik, and F. Hutter · 2021
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HPOBench: A collection of reproducible multi-fidelity benchmark problems for HPO
K. Eggensperger, P. Müller, N. Mallik, M. Feurer, R. Sass, A. Klein, N. Awad, M. Lindauer, and F. Hutter · 2021
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The hypervolume indicator: Computational problems and algorithms
Andreia P Guerreiro, Carlos M Fonseca, and Luís Paquete · 2021
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Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Cited alongside, same era.
A tutorial on multiobjective optimization: fundamentals and evolutionary methods
M. Emmerich and A. Deutz · 2018
Cited alongside, same era.
BOHB: Robust and efficient Hyperparameter Optimization at scale
S. Falkner, A. Klein, and F. Hutter · 2018
Cited alongside, same era.
Investigating the normalization procedure of nsga-iii
Julian Blank, Kalyanmoy Deb, and Proteek Chandan Roy · 2019
Cited alongside, same era.
Neural Architecture Search: A survey
T. Elsken, J. Metzen, and F. Hutter · 2019
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Hyperparameter Optimization
M. Feurer and F. Hutter · 2019
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An adaptive evolutionary algorithm based on non-euclidean geometry for many-objective optimization
Annibale Panichella · 2019
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Bag of baselines for multi-objective joint neural architecture search and hyperparameter optimization
Sergio Izquierdo, Julia Guerrero-Viu, Sven Hauns, Guilherme Miotto, Simon Schrodi, André Biedenkapp, Thomas Elsken, Difan Deng, Marius Lindauer, and Frank Hutter · 2021
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A multi-objective perspective on jointly tuning hardware and hyperparameters, 2021
D. Salinas, V. Perrone, O. Cruchant, and C. Archambeau · 2021
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A multi-objective perspective on jointly tuning hardware and hyperparameters
David Salinas, Valerio Perrone, Olivier Cruchant, and Cedric Archambeau · 2021
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Multi-objective asynchronous successive halving, 2021
R. Schmucker, M. Donini, M. Zafar, D. Salinas, and C. Archambeau · 2021
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Multi-objective asynchronous successive halving
Robin Schmucker, Michele Donini, Muhammad Bilal Zafar, David Salinas, and Cédric Archambeau · 2021
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Jahs-bench-201: A foundation for research on joint architecture and hyperparameter search
Archit Bansal, Danny Stoll, Maciej Janowski, Arber Zela, and Frank Hutter · 2022
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Multi-objective hyperparameter optimization–an overview
F. Karl, T. Pielok, J. Moosbauer, F. Pfisterer, S. Coors, M. Binder, L. Schneider, J. Thomas, J. Richter, M. Lang, G. Eduardo, B. Juergen, and B. Bischl · 2022
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Smac3: A versatile bayesian optimization package for hyperparameter optimization
Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, and Frank Hutter · 2022
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Yahpo gym-an efficient multi-objective multi-fidelity benchmark for hyperparameter optimization
F. Pfisterer, L. Schneider, J. Moosbauer, M. Binder, and B. Bischl · 2022
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Surrogate NAS benchmarks: Going beyond the limited search spaces of tabular NAS benchmarks
A. Zela, J. Siems, L. Zimmer, J. Lukasik, M. Keuper, and F. Hutter · 2022
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