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To achieve peak predictive performance, hyperparameter optimization (HPO) is a crucial component of machine learning and its applications.
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Fast Bayesian optimization of machine learning hyperparameters on large datasets
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K. Kandasamy, G. Dasarathy, J. Schneider, and B. Póczos · 2017
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D. Golovin, B. Solnik, S. Moitra, G. Kochanski, J. Karro, and D. Sculley · 2017
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The (black) art of runtime evaluation: Are we comparing algorithms or implementations?
H. Kriegel, E. Schubert, and A. Zimek · 2017
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Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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C. Doerr, H. Wang, F. Ye, S. van Rijn, and T. Bäck · 2018
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Differential evolution for neural architecture search
N. Awad, N. Mallik, and F. Hutter · 2020
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Practical multi-fidelity Bayesian optimization for hyperparameter tuning
J. Wu, S. Toscano-Palmerin, P. Frazier, and A. Wilson · 2020
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Multi-fidelity Bayesian optimization with max-value entropy search and its parallelization
S. Takeno, H. Fukuoka, Y. Tsukada, T. Koyama, M. Shiga, I. Takeuchi, and M. Karasuyama · 2020
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Best practices for scientific research on neural architecture search
M. Lindauer and F. Hutter · 2020
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Benchmarking in optimization: Best practice and open issues
T. Bartz-Beielstein, C. Doerr, J. Bossek, S. Chandrasekaran, T. Eftimov, A. Fischbach, P. Kerschke, M. López-Ibáñez, K. Malan, J. Moore, B. Naujoks, P. Orzechowski, V. Volz, M. Wagner, and T. Weise · 2020
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Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019
Z. Liu, A. Pavao, Z. Xu, S. Escalera, F. Ferreira, I. Guyon, S. Hong, F. Hutter, R. Ji, J. Jacques Junior, G. Li, M. Lindauer, Z. Luo, M. Madadi, T. Nierhoff, K. Niu, C. Pan, D. Stoll, S. Treguer, J. Wang, P. Wang, C. Wu, , Y. Xiong, A. Zela, and Y. Zhang · 2020
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Using a thousand optimization tasks to learn hyperparameter search strategies
L. Metz, N. Maheswaranathan, R. Sun, C. Freeman, B. Poole, and J. Sohl-Dickstein · 2020
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Mixed-variable bayesian optimization
E. Daxberger, A. Makarova, M. Turchetta, and A. Krause · 2020
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NAS-Bench-301 and the case for surrogate benchmarks for neural architecture search
J. Siems, L. Zimmer, A. Zela, J. Lukasik, M. Keuper, and F. Hutter · 2020
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Tuning hyperparameters without grad students: Scalable and robust Bayesian optimisation with Dragonfly
K. Kandasamy, K. Vysyaraju, W. Neiswanger, B. Paria, C. Collins, J. Schneider, B. Poczos, and E. Xing · 2020
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BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
M. Balandat, B. Karrer, D. Jiang, S. Daulton, B. Letham, A. Wilson, and E. Bakshy · 2020
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Sherpa: Robust hyperparameter optimization for machine learning
L. Hertel, J. Collado, P. Sadowski, J. Ott, and P. Baldi · 2020
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Published online: iclr.cc
Proceedings of the International Conference on Learning Representations (ICLR’20) , 2020 · 2020
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T. Gebru, J. Morgenstern, B. Vecchione, J. Vaughan, H. Wallach, H. Daumé III, and K. Crawford · 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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EXPObench: Benchmarking surrogate-based optimisation algorithms on expensive black-box functions
L. Bliek, A. Guijt, R. Karlsson, S. Verwer, and M. de Weerdt · 2021
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Olympus: a benchmarking framework for noisy optimization and experiment planning
F. Häse, M. Aldeghi, R. Hickman, L. Roch, M. Christensen, E. Liles, J. Hein, and A. Aspuru-Guzik · 2021
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Do transformer modifications transfer across implementations and applications?
S. Narang, H. Chung, Y. Tay, W. Fedus, T. Fevry, M. Matena, K. Malkan, N. Fiedel, N. Shazeer, Z. Lan, Y. Zhou, W. Li, N. Ding, J. Marcus, A. Roberts, and C. Raffel · 2021
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X. Bouthillier, P. Delaunay, M. Bronzi, A. Trofimov, B. Nichyporuk, J. Szeto, N. Mohammadi Sepahvand, E. Raff, K. Madan, V. Voleti, S. Ebrahimi Kahou, V. Michalski, T. Arbel, C. Pal, G. Varoquaux, and P. Vincent · 2021
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Bayesian optimization is superior to random search for machine learning hyperparameter tuning: Analysis of the black-box optimization challenge 2020
R. Turner, D. Eriksson, M. McCourt, J. Kiili, E. Laaksonen, Z. Xu, and I. Guyon · 2021
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Amortized auto-tuning: Cost-efficient transfer optimization for hyperparameter recommendation
Y. Xiao, E. Xing, and W. Neiswanger · 2021
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LassoBench: A high-dimensional hyperparameter optimization benchmark suite for lasso
K. Šehić, A. Gramfort, J. Salmon, and L. Nardi · 2021
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YAHPO Gym – design criteria and a new multifidelity benchmark for hyperparameter optimization
F. Pfisterer, L. Schneider, J. Moosbauer, M. Binder, and B. Bischl · 2021
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Data vs classifiers, who wins?
L. Cardoso, V. Santos, R. Francês, R. Prudêncio, and R. Alves · 2021
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Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks , 2021
J. Vanschoren, S. Yeung, and M. Xenochristou, editors · 2021
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SMAC3: A versatile Bayesian optimization package for hyperparameter optimization
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HEBO: Pushing the limits of sample-efficient hyper-parameter optimisation
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