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While deep learning has celebrated many successes, its results often hinge on the meticulous selection of hyperparameters (HPs).
Tabular benchmarks for joint architecture and hyperparameter optimization
Klein, A. and Hutter, F. (2019) · 1905
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NAS-Bench-201: Extending the scope of reproducible neural architecture search
Dong, X. and Yang, Y. (2020) · 2001
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Algorithms for hyper-parameter optimization
Bergstra, J., Bardenet, R., Bengio, Y., and Kégl, B. (2011) · 2011
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Efficient benchmarking of hyperparameter optimizers via surrogates
Eggensperger, K., Hutter, F., Hoos, H., and Leyton-Brown, K. (2015) · 2015
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Non-stochastic best arm identification and hyperparameter optimization
Jamieson, K. and Talwalkar, A. (2016) · 2016
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Multi-fidelity Bayesian optimisation with continuous approximations
Kandasamy, K., Dasarathy, G., Schneider, J., and Póczos, B. (2017) · 2017
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HyperBand: A novel bandit-based approach to hyperparameter optimization
Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., and Talwalkar, A. (2017) · 2017
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BOHB: Robust and efficient hyperparameter optimization at scale
Falkner, S., Klein, A., and Hutter, F. (2018) · 2018
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Tune: A research platform for distributed model selection and training
Liaw, R., Liang, E., Nishihara, R., Moritz, P., Gonzalez, J., and Stoica, I. (2018) · 2018
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Optuna: A next-generation hyperparameter optimization framework
Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019) · 2019
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Tuning hyperparameters without grad students: Scalable and robust Bayesian optimisation with Dragonfly
Kandasamy, K., Vysyaraju, K., Neiswanger, W., Paria, B., Collins, C., Schneider, J., Poczos, B., and Xing, E. (2020) · 2020
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A system for massively parallel hyperparameter tuning
Li, L., Jamieson, K., Rostamizadeh, A., Gonina, E., Ben-Tzur, J., Hardt, M., Recht, B., and Talwalkar, A. (2020) · 2020
Cited alongside, same era.
Multiobjective tree-structured Parzen estimator for computationally expensive optimization problems
Ozaki, Y., Tanigaki, Y., Watanabe, S., and Onishi, M. (2020) · 2020
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HPO-B: A large-scale reproducible benchmark for black-box HPO based on OpenML
Arango, S., Jomaa, H., Wistuba, M., and Grabocka, J. (2021) · 2021
Cited alongside, same era.
DEHB: Evolutionary HyperBand for scalable, robust and efficient hyperparameter optimization
Awad, N., Mallik, N., and Hutter, F. (2021) · 2021
Cited alongside, same era.
HPOBench: A collection of reproducible multi-fidelity benchmark problems for HPO
SMAC3: A versatile Bayesian optimization package for hyperparameter optimization
Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp, A., Deng, D., Benjamins, C., Ruhkopf, T., Sass, R., and Hutter, F. (2022) · 2022
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NAS-Bench-Suite: NAS evaluation is (now) surprisingly easy
Mehta, Y., White, C., Zela, A., Krishnakumar, A., Zabergja, G., Moradian, S., Safari, M., Yu, K., and Hutter, F. (2022) · 2022
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Multiobjective tree-structured Parzen estimator
Ozaki, Y., Tanigaki, Y., Watanabe, S., Nomura, M., and Onishi, M. (2022) · 2022
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YAHPO Gym – an efficient multi-objective multi-fidelity benchmark for hyperparameter optimization
Pfisterer, F., Schneider, L., Moosbauer, J., Binder, M., and Bischl, B. (2022) · 2022
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Syne Tune: A library for large scale hyperparameter tuning and reproducible research
Salinas, D., Seeger, M., Klein, A., Perrone, V., Wistuba, M., and Archambeau, C. (2022) · 2022
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Eggensperger, K., Müller, P., Mallik, N., Feurer, M., Sass, R., Klein, A., Awad, N., Lindauer, M., and Hutter, F. (2021) · 2021
Cited alongside, same era.
TrivialAugment: Tuning-free yet state-of-the-art data augmentation
Müller, S. and Hutter, F. (2021) · 2021
Cited alongside, same era.
On the importance of hyperparameter optimization for model-based reinforcement learning
Zhang, B., Rajan, R., Pineda, L., Lambert, N., Biedenkapp, A., Chua, K., Hutter, F., and Calandra, R. (2021) · 2021
Cited alongside, same era.
Auto-PyTorch: Multi-fidelity metalearning for efficient and robust AutoDL
Zimmer, L., Lindauer, M., and Hutter, F. (2021) · 2021
Cited alongside, same era.
JAHS-Bench-201: A foundation for research on joint architecture and hyperparameter search
Bansal, A., Stoll, D., Janowski, M., Zela, A., and Hutter, F. (2022) · 2022
Cited alongside, same era.
HEBO: Pushing the limits of sample-efficient hyper-parameter optimisation
Cowen-Rivers, A., Lyu, W., Tutunov, R., Wang, Z., Grosnit, A., Griffiths, R., Maraval, A., Jianye, H., Wang, J., Peters, J., et al. (2022) · 2022
Cited alongside, same era.
Hyper-Tune: towards efficient hyper-parameter tuning at scale
Li, Y., Shen, Y., Jiang, H., Zhang, W., Li, J., Liu, J., Zhang, C., and Cui, B. (2022) · 2022
Cited alongside, same era.
Python wrapper for simulating multi-fidelity optimization on HPO benchmarks without any wait
Watanabe, S. (2023a)
Cited in the paper.
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On the importance of architectures and hyperparameters for fairness in face recognition
Sukthanker, R., Dooley, S., Dickerson, J., White, C., Hutter, F., and Goldblum, M. (2022) · 2022
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On the importance of hyperparameters and data augmentation for self-supervised learning
Wagner, D., Ferreira, F., Stoll, D., Schirrmeister, R., Müller, S., and Hutter, F. (2022) · 2022
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Watanabe, S. and Hutter, F. (2022) · 2022
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c-TPE: tree-structured Parzen estimator with inequality constraints for expensive hyperparameter optimization
Watanabe, S. and Hutter, F. (2023) · 2023
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Multi-fidelity methods for optimization: A survey
Li, K. and Li, F. (2024) · 2024
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