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When developing and analyzing new hyperparameter optimization methods, it is vital to empirically evaluate and compare them on well-curated benchmark suites.
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A. Klein, S. Falkner, S. Bartels, P. Hennig, and F. Hutter · 2017
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batchtools: Tools for R to work on batch systems
M. Lang, B. Bischl, and D. Surmann · 2017
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Advanced Research Computing Center · 2018
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Efficient benchmarking of algorithm configurators via model-based surrogates
K. Eggensperger, M. Lindauer, H. H. Hoos, F. Hutter, and K. Leyton-Brown · 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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Hyperband: A novel bandit-based approach to hyperparameter optimization
L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, and A. Talwalkar · 2018
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V. Perrone, R. Jenatton, M. W. Seeger, and C. Archambeau · 2018
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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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Practical transfer learning for Bayesian optimization
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Meta-learning for symbolic hyperparameter defaults
P. Gijsbers, F. Pfisterer, J. N. van Rijn, B. Bischl, and J. Vanschoren · 2021
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Revisiting deep learning models for tabular data
Y. Gorishniy, I. Rubachev, V. Khrulkov, and A. Babenko · 2021
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Bag of baselines for multi-objective joint neural architecture search and hyperparameter optimization
J. Guerrero-Viu, S. Hauns, S. Izquierdo, G. Miotto, S. Schrodi, A. Biedenkapp, T. Elsken, D. Deng, M. Lindauer, and F. Hutter · 2021
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COCO: A platform for comparing continuous optimizers in a black-box setting
N. Hansen, A. Auger, R. Ros, O. Mersmann, T. Tušar, and D. Brockhoff · 2021
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Automated benchmark-driven design and explanation of hyperparameter optimizers
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Over-optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results
C. Nießl, M. Herrmann, C. Wiedemann, G. Casalicchio, and A.-L. Boulesteix · 2021
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F. Pfisterer, J. N. van Rijn, P. Probst, A. C. Müller, and B. Bischl · 2021
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HPO-B: A large-scale reproducible benchmark for black-box HPO based on OpenML
S. Pineda Arango, H. S. Jomaa, M. Wistuba, and J. Grabocka · 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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BANANAS: Bayesian optimization with neural architectures for neural architecture search
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Auto-pytorch tabular: Multi-fidelity metalearning for efficient and robust AutoDL
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SMAC3: A versatile Bayesian optimization package for hyperparameter optimization
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Kurobako
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R. Turner · 2022
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