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Automated machine learning (AutoML) strives for the automatic configuration of machine learning algorithms and their composition into an overall (software) solution - a machine learning pipeline - tailored to the learning task (dataset) at hand.
“Fast Subsampling Performance Estimates for Classification Algorithm Selection”
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M. Gendreau · 2003
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“A Gender-Based Genetic Algorithm for the Automatic Configuration of Algorithms”
C. Ans“’otegui, M. Sellmann and K. Tierney · 2009
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“ParamILS: An Automatic Algorithm Configuration Framework”
F. Hutter, H. Hoos, K. Leyton-Brown and T. St“”utzle · 2009
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“Predicting Execution Time of Computer Programs Using Sparse Polynomial Regression”
L. Huang, J. Jia, B. Yu, B. Chun, P. Maniatis and M. Naik · 2010
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“Transfer Learning”
L. Torrey and J. Shavlik · 2010
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“Algorithms for Hyper-Parameter Optimization”
J. Bergstra, R. Bardenet, Y. Bengio and B. K“’egl · 2011
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“Sequential Model-Based Optimization for General Algorithm Configuration”
F. Hutter, H. Hoos and K. Leyton-Brown · 2011
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“One Shot Learning of Simple Visual Concepts”
B. Lake, R. Salakhutdinov, J. Gross and J. Tenenbaum · 2011
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“A Meta-Mining Infrastructure to Support KD Workflow Optimization”
P. Nguyen, A. Kalousis and M. Hilario · 2011
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“Discovering the Suitability of Optimisation Algorithms by Learning from Evolved Instances”
K. Smith-Miles and J. van Hemert · 2011
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“Early Stopping - But When?”
L. Prechelt · 2012
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“Practical Bayesian Optimization of Machine Learning Algorithms”
J. Snoek, H. Larochelle and R. Adams · 2012
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“Collaborative Hyperparameter Tuning”
R. Bardenet, M. Brendel, B. K“’egl and M. Sebag · 2013
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“A Systematic Literature Review for Software Sustainability Measures”
C. Calero, M. Bertoa and M.“’A. Moraga · 2013
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“Multi-Task Bayesian Optimization”
K. Swersky, J. Snoek and R. Adams · 2013
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“Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms”
C. Thornton, R. Hutter, H. Hoos and K. Leyton-Brown · 2013
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“OpenML: Networked Science in Machine Learning”
J. Vanschoren, J. van Rijn, B. Bischl and L. Torgo · 2013
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“Algorithm Runtime Prediction: Methods & Evaluation”
F. Hutter, L. Xu, H. Hoos and K. Leyton-Brown · 2014
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“Hyperopt-Sklearn: Automatic Hyperparameter Configuration for scikit-learn”
B. Komer, J. Bergstra and C. Eliasmith · 2014
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“Freeze-Thaw Bayesian Optimization”
K. Swersky, J. Snoek and R. Adams · 2014
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“Efficient Transfer Learning Method for Automatic Hyperparameter Tuning”
D. Yogatama and G. Mann · 2014
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“Speeding up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves”
T. Domhan, J. Springenberg and F. Hutter · 2015
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“Efficient and Robust Automated Machine Learning”
M. Feurer, A. Klein, K. Eggensperger, J. Springenberg, M. Blum and F. Hutter · 2015
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“Framing Sustainability as a Property of Software Quality”
P. Lago, S. Kocak, I. Crnkovic and B. Penzenstadler · 2015
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“Non-Stochastic Best Arm Identification and Hyperparameter Optimization”
K. Jamieson and A. Talwalkar · 2016
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“The FAIR Guiding Principles for Scientific Data Management and Stewardship”
M. Wilkinson, M. Dumontier, I. Aalbersberg, G. Appleton, M. Axton, A. Baak, N. Blomberg, J. Boiten, L. da Silva, P. Bourne, J. Bouwman, A. Brookes, T. Clark, M. Crosas, I. Dillo, O. Dumon, S. Edmunds, C. Evelo, R. Finkers, A. Gonzalez-Beltran, A. Gray, P. Groth, C. Goble, J. Grethe, J. Heringa, P. ’t Hoen, R. Hooft, T. Kuhn, R. Kok, J. Kok, S. Lusher, M. Martone, A. Mons, A. Packer, B. Persson, P. Rocca-Serra, M. Roos, R. van Schaik, S. Sansone, E. Schultes, T. Sengstag, T. Slater, G. Strawn, MA. Swertz, M. Thompson, J. van Lei, E. van Mulligen, J. Velterop, A. Waagmeester, P. Wittenburg, K. Wolstencroft, J. Zhao and B. Mons · 2016
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“Multi-Fidelity Bayesian Optimisation with Continuous Approximations”
K. Kandasamy, G. Dasarathy, J. Schneider and B. P“’oczos · 2017
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“Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets”
A. Klein, S. Falkner, S. Bartels, P. Hennig and F. Hutter · 2017
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“Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization”
L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh and A. Talwalkar · 2017
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“Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start”
V. Perrone, R. Jenatton, M. Seeger and C. Archambeau · 2017
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“Zero-Shot Learning - The Good, the Bad and the Ugly”
Y. Xian, B. Schiele and Z. Akata · 2017
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“Neural Architecture Search with Reinforcement Learning”
B. Zoph and Q. Le · 2017
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“Benchmarking Automatic Machine Learning Frameworks”
A. Balaji and A. Allen · 2018
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“BOHB: Robust and Efficient Hyperparameter Optimization at Scale”
S. Falkner, A. Klein and F. Hutter · 2018
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“Practical Automated Machine Learning for the AutoML Challenge 2018”
M. Feurer, K. Eggensperger, S. Falkner, M. Lindauer and F. Hutter · 2018
Cited alongside, same era.
“Probabilistic Matrix Factorization for Automated Machine Learning”
N. Fusi, R. Sheth and M. Elibol · 2018
Cited alongside, same era.
“AMC: AutoML for Model Compression and Acceleration on Mobile Devices”
Y. He, J. Lin, Z. Liu, H. Wang, L. Li and S. Han · 2018
Cited alongside, same era.
“Warmstarting of Model-Based Algorithm Configuration”
M. Lindauer and F. Hutter · 2018
Cited alongside, same era.
“Progressive Neural Architecture Search”
C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L. Li, L. Fei-Fei, A. Yuille, J. Huang and K. Murphy · 2018
Cited alongside, same era.
“ML-Plan: Automated Machine Learning via Hierarchical Planning”
F. Mohr, M. Wever and E. H“”ullermeier · 2018
“NAS-Bench-1Shot1: Benchmarking and Dissecting One-Shot Neural Architecture Search”
A. Zela, J. Siems and F. Hutter · 2020
Later among the works it cites.
“DEHB: Evolutionary Hyberband for Scalable, Robust and Efficient Hyperparameter Optimization”
N. Awad, N. Mallik and F. Hutter · 2021
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“On the Dangers of Stochastic Parrots: Can Language Models be too big?”
E. Bender, T. Gebru, A. McMillan-Major and S. Shmitchell · 2021
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“Green Machine Learning via Augmented Gaussian Processes and Multi-Information Source Optimization”
A. Candelieri, R. Perego and F. Archetti · 2021
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“IrEne: Interpretable Energy Prediction for Transformers”
Q. Cao, Y. Lal, H. Trivedi, A. Balasubramanian and N. Balasubramanian · 2021
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“Socially Responsible AI Algorithms: Issues, Purposes, and Challenges”
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“Characterizing Classification Datasets: a Study of Meta-Features for Meta-Learning”
A. Rivolli, L. Garcia, C. Soares, J. Vanschoren and A. de Carvalho · 2018
Cited alongside, same era.
“HyperPower: Power and Memory Constrained Hyperparameter Optimization for Neural Networks”
D. Stamoulis, E. Cai, D. Juan and D. Marculescu · 2018
Cited alongside, same era.
“Designing Adaptive Neural Networks for Energy-Constrained Image Classification”
D. Stamoulis, T. Chin, A. Prakash, H. Fang, S. Sajja, M. Bognar and D. Marculescu · 2018
Cited alongside, same era.
“Meta-Learning: A Survey”
J. Vanschoren · 2018
Cited alongside, same era.
“5Ws of Green and Sustainable Software”
C. Calero, J. Mancebo, F. Garc“’a, M.“’A. Moraga, J. Bern“’a, J. Fern“’andez-Alem“’an and A. Toval · 2019
Cited alongside, same era.
“AutoML using Metadata Language Embeddings”
I. Drori, L. Liu, Y. Nian, S. Koorathota, J. Li, A. Moretti, J. Freire and M. Udell · 2019
Cited alongside, same era.
L. Cheng, K. Varshney and H. Liu · 2021
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“MetaDL Challenge Design and Baseline Results”
A. El Baz, I. Guyon, Z. Liu, J. van Rijn, S. Treguer and J. Vanschoren · 2021
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“Green Algorithms: Quantifying the Carbon Footprint of Computation”
L. Lannelongue, J. Grealey and M. Inouye · 2021
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“HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark”
C. Li, Z. Yu, Y. Fu, Y. Zhang, Y. Zhao, H. You, Q. Yu, Y. Wang, C. Hao and Y. Lin · 2021
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“Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition”
M. Lin, P. Wang, Z. Sun, H. Chen, X. Sun, Q. Qian, H. Li and R. Jin · 2021
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“Engineering Design Optimization”
J. Martins and A. Ning · 2021
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“Neural Architecture Search without Training”
J. Mellor, J. Turner, A. Storkey and E. Crowley · 2021
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“Towards Model Selection using Learning Curve Cross-Validation”
F. Mohr and J. van Rijn · 2021
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“Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning”
F. Mohr, M. Wever, A. Tornede and E. H“”ullermeier · 2021
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“The Energy and Carbon Footprint of Training End-to-End Speech Recognizers”
T. Parcollet and M. Ravanelli · 2021
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“Carbon Emissions and Large Neural Network Training”
D. Patterson, J. Gonzalez, Q. Le, C. Liang, L. Munguia, D. Rothchild, D. So, M. Texier and J. Dean · 2021
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“Machine Learning Assisted Characterisation and Simulation of Compressive Damage in Composite Laminates”
J. Reiner, R. Vaziri and N. Zobeiry · 2021
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“LeanML: A Design Pattern To Slash Avoidable Wastes in Machine Learning Projects”
Y. Samo · 2021
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“CodeCarbon: Estimate and Track Carbon Emissions from Machine Learning Computing”
V. Schmidt, K. Goyal, A. Joshi, B. Feld, L. Conell, N. Laskaris, D. Blank, J. Wilson, S. Friedler and S. Luccioni · 2021
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“Multi-Objective Asynchronous Successive Halving”
R. Schmucker, M. Donini, M. Zafar, C. Salinas and C. Archambeau · 2021
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“EnergyVis: Interactively Tracking and Exploring Energy Consumption for ML Models”
O. Shaikh, J. Saad-Falcon, A. Wright, N. Das, S. Freitas, O. Asensio and D. Chau · 2021
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“Privileged Zero-Shot AutoML”
N. Singh, B. Kates, J. Mentch, A. Kharkar, M. Udell and I. Drori · 2021
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“Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance”
T. Tornede, A. Tornede, M. Wever and E. H“”ullermeier · 2021
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“AutoML for Multi-Label Classification: Overview and Empirical Evaluation”
M. Wever, A. Tornede, F. Mohr and E. H“”ullermeier · 2021
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“Sustainable AI: AI for Sustainability and the Sustainability of AI”
A. van Wynsberghe · 2021
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“Auto-Pytorch: Multi-Fidelity Meta Learning for Efficient and Robust AutoDL”
L. Zimmer, M. Lindauer and F. Hutter · 2021
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“Benchmark and Survey of Automated Machine Learning Frameworks”
M. Z“”oller and M. Huber · 2021
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“A Survey on Sustainable Surrogate-Based Optimisation”
L. Bliek · 2022
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“Fair and Green Hyperparameter Optimization via Multi-objective and Multiple Information Source Bayesian Optimization”
A. Candelieri, A. Ponti and F. Archetti · 2022
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“Auto-sklearn 2.0: Hands-Free AutoML via Meta-Learning”
M. Feurer, K. Eggensperger, S. Falkner, M. Lindauer and F. Hutter · 2022
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“NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing”
N. Klyuchnikov, I. Trofimov, E. Artemova, M. Salnikov, M. Fedorov, A. Filippov and E. Burnaev · 2022
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“Automatic Termination for Hyperparameter Optimization”
A. Makarova, H. Shen, V. Perrone, A. Klein, J. Faddoul, A. Krause, M. Seeger and C. Archambeau · 2022
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“The Carbon Footprint of Machine Learning Training will Plateau, then Shrink”
D. Patterson, J. Gonzalez, U. H“”olzle, Q. Le, C. Liang, L. Munguia, D. Rothchild, D. So, M. Texier and J. Dean · 2022
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“AutoML for Climate Change: A Call to Action”
R. Tu, N. Roberts, V. Prasad, S. Nayak, P. Jain, F. Sala, G. Ramakrishnan, A. Talwalkar, W. Neiswanger and C. White · 2022
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“Towards Green Automated Machine Learning: Status Quo and Future Directions”
T. Tornede, A. Tornede, J. Hanselle, F. Mohr, M. Wever and E. H“”ullermeier · 2023
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