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Comparing different AutoML frameworks is notoriously challenging and often done incorrectly.
The weka data mining software: An update
M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I.H. Witten · 1931
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Genetic programming: on the programming of computers by means of natural selection , volume 1
John R Koza · 1992
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Umcp: A sound and complete procedure for hierarchical task-network planning
Kutluhan Erol, James A Hendler, and Dana S Nau · 1994
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Nas-bench-1shot1: Benchmarking and dissecting one-shot neural architecture search
Arber Zela, Julien Siems, and Frank Hutter · 2001
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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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Ensemble selection from libraries of models
Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew, and Alex Ksikes · 2004
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Benchmarking least squares support vector machine classifiers
Tony Van Gestel, Johan AK Suykens, Bart Baesens, Stijn Viaene, Jan Vanthienen, Guido Dedene, Bart De Moor, and Joos Vandewalle · 2004
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Getting the most out of ensemble selection
Rich Caruana, Art Munson, and Alexandru Niculescu-Mizil · 2006
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Statistical Comparisons of Classifiers over Multiple Data Sets
J. Demšar · 2006
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Auto-pytorch tabular: Multi-fidelity metalearning for efficient and robust autodl
Lucas Zimmer, Marius Lindauer, and Frank Hutter · 2006
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Auto-sklearn 2.0: Hands-free automl via meta-learning, 2020
Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, and Frank Hutter · 2007
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Generalized m-fluctuation tests for parameter instability
Achim Zeileis and Kurt Hornik · 2007
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Nas-bench-301 and the case for surrogate benchmarks for neural architecture search
Julien Siems, Lucas Zimmer, Arber Zela, Jovita Lukasik, Margret Keuper, and Frank Hutter · 2008
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Particle swarm model selection
Hugo Jair Escalante, Manuel Montes, and Luis Enrique Sucar · 2009
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Grammar-based genetic programming: a survey
Robert I McKay, Nguyen Xuan Hoai, Peter Alexander Whigham, Yin Shan, and Michael O’neill · 2010
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Implementations of algorithms for hyper-parameter optimization
James Bergstra, Rémi Bardenet, B Kégl, and Y Bengio · 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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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, et al · 2011
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Accounting for individual differences in bradley-terry models by means of recursive partitioning
Carolin Strobl, Florian Wickelmaier, and Achim Zeileis · 2011
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Meta-learning for evolutionary parameter optimization of classifiers
Matthias Reif, Faisal Shafait, and Andreas Dengel · 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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Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
James Bergstra, Daniel Yamins, and David Cox · 2013
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Towards an Empirical Foundation for Assessing Bayesian Optimization of Hyperparameters
K. Eggensperger, M. Feurer, F. Hutter, J. Bergstra, J. Snoek, H. Hoos, and K. Leyton-Brown · 2013
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H2O: Scalable Machine Learning Platform , 2013
H2O.ai · 2013
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Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms
C. Thornton, F. Hutter, H. H. Hoos, and K. Leyton-Brown · 2013
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Preliminary Evaluation of Hyperopt Algorithms on HPOLib
James Bergstra, Brent Komer, Chris Eliasmith, and David Warde-Farley · 2014
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(psycho-)analysis of benchmark experiments
Manuel J.A. Eugster, Friedrich Leisch, and Carolin Strobl · 2014
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Runtime and memory consumption analyses for machine learning r programs
Helena Kotthaus, Ingo Korb, Michel Lang, Bernd Bischl, Jörg Rahnenführer, and Peter Marwedel · 2014
Cited alongside, same era.
OpenML: Networked science in machine learning
J. Vanschoren, J. van Rijn, B. Bischl, and L. Torgo · 2014
Cited alongside, same era.
Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
Cited alongside, same era.
Non-stochastic best arm identification and hyperparameter optimization
Kevin Jamieson and Ameet Talwalkar · 2016
Cited alongside, same era.
Tpot: A tree-based pipeline optimization tool for automating machine learning
Randal S. Olson and Jason H. Moore · 2016
Cited alongside, same era.
Evaluation of a tree-based pipeline optimization tool for automating data science
Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools
Anh Truong, Austin Walters, Jeremy Goodsitt, Keegan E. Hines, C. Bayan Bruss, and Reza Farivar · 2019
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NAS-bench-101: Towards reproducible neural architecture search
Chris Ying, Aaron Klein, Eric Christiansen, Esteban Real, Kevin Murphy, and Frank Hutter · 2019
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Autogluon-tabular: Robust and accurate automl for structured data, 2020
Nick Erickson, Jonas Mueller, Alexander Shirkov, Hang Zhang, Pedro Larroy, Mu Li, and Alexander Smola · 2020
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H2O AutoML: Scalable automatic machine learning
Erin LeDell and Sebastien Poirier · 2020
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Importance of tuning hyperparameters of machine learning algorithms
Hilde JP Weerts, Andreas C Mueller, and Joaquin Vanschoren · 2020
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Randal S. Olson, Nathan Bartley, Ryan J. Urbanowicz, and Jason H. Moore · 2016
Cited alongside, same era.
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
Cited alongside, same era.
Pmlb: a large benchmark suite for machine learning evaluation and comparison
Randal S Olson, William La Cava, Patryk Orzechowski, Ryan J Urbanowicz, and Jason H Moore · 2017
Cited alongside, same era.
Toward the automated analysis of complex diseases in genome-wide association studies using genetic programming
Andrew Sohn, Randal S Olson, and Jason H Moore · 2017
Cited alongside, same era.
Benchmarking automatic machine learning frameworks
A. Balaji and A. Allen · 2018
Cited alongside, same era.
Alphad3m: Machine learning pipeline synthesis
Iddo Drori, Yamuna Krishnamurthy, Remi Rampin, Raoni Lourenço, Jorge One, Kyunghyun Cho, Claudio Silva, and Juliana Freire · 2018
Cited alongside, same era.
Practical automated machine learning for the automl challenge 2018
Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, and Frank Hutter · 2018
Cited alongside, same era.
Sebastian Pineda Arango, Hadi S. Jomaa, Martin Wistuba, and Josif Grabocka · 2021
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Openml benchmarking suites
Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Pieter Gijsbers, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan N van Rijn, and Joaquin Vanschoren · 2021
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Hpobench: A collection of reproducible multi-fidelity benchmark problems for hpo, 2021
Katharina Eggensperger, Philipp Müller, Neeratyoy Mallik, Matthias Feurer, René Sass, Aaron Klein, Noor Awad, Marius Lindauer, and Frank Hutter · 2021
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Protein abundance prediction through machine learning methods
Mauricio Ferreira, Rafaela Ventorim, Eduardo Almeida, Sabrina Silveira, and Wendel Silveira · 2021
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GAMA: A General Automated Machine Learning Assistant
Pieter Gijsbers and Joaquin Vanschoren · 2021
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Coco: A platform for comparing continuous optimizers in a black-box setting
Nikolaus Hansen, Anne Auger, Raymond Ros, Olaf Mersmann, Tea Tušar, and Dimo Brockhoff · 2021
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Mljar: State-of-the-art automated machine learning framework for tabular data. version 0.10.3, 2021
Aleksandra Płońska and Piotr Płoński · 2021
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Tpot-nn: augmenting tree-based automated machine learning with neural network estimators
Joseph D Romano, Trang T Le, Weixuan Fu, and Jason H Moore · 2021
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Multi-objective asynchronous successive halving, 2021
Robin Schmucker, Michele Donini, Muhammad Bilal Zafar, David Salinas, and Cédric Archambeau · 2021
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Lassobench: A high-dimensional hyperparameter optimization benchmark suite for lasso
Kenan Šehić, Alexandre Gramfort, Joseph Salmon, and Luigi Nardi · 2021
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Benchmarking multimodal automl for tabular data with text fields
Xingjian Shi, Jonas Mueller, Nick Erickson, Mu Li, and Alexander J Smola · 2021
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Lightautoml: Automl solution for a large financial services ecosystem
Anton Vakhrushev, Alexander Ryzhkov, Maxim Savchenko, Dmitry Simakov, Rinchin Damdinov, and Alexander Tuzhilin · 2021
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Automl adoption in ml software
Koen Van der Blom, Alex Serban, Holger Hoos, and Joost Visser · 2021
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Flaml: A fast and lightweight automl library
Chi Wang, Qingyun Wu, Markus Weimer, and Erkang Zhu · 2021
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AutoML for Multi-Label Classification: Overview and Empirical Evaluation
M. Wever, A. Tornede, F. Mohr, and E. Hullermeier · 2021
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Frugal optimization for cost-related hyperparameters
Qingyun Wu, Chi Wang, and Silu Huang · 2021
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Benchmark and survey of automated machine learning frameworks
Marc-André Zöller and Marco F. Huber · 2021
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Kurobako
Takeru Ohta and Hiroyuki Vincent Yamazaki · 2022
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Uber. bayesopt benchmark
Ryan Turner · 2022
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OpenML-CTR23–a curated tabular regression benchmarking suite
Sebastian Felix Fischer, Matthias Feurer, and Bernd Bischl · 2023
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Fast and informative model selection using learning curve cross-validation
Felix Mohr and Jan N van Rijn · 2023
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Naive automated machine learning
Felix Mohr and Marcel Wever · 2023
Closest in time.