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Hyperparameter (HP) optimization of deep learning (DL) is essential for high performance.
Tabular benchmarks for joint architecture and hyperparameter optimization
Klein, A. and Hutter, F. (2019) · 1905
Earlier work this paper cites.
NAS-Bench-201: Extending the scope of reproducible neural architecture search
Dong, X. and Yang, Y. (2020) · 2001
Earlier work this paper cites.
Algorithms for hyper-parameter optimization
Bergstra, J., Bardenet, R., Bengio, Y., and Kégl, B. (2011) · 2011
Earlier work this paper cites.
Efficient benchmarking of hyperparameter optimizers via surrogates
Eggensperger, K., Hutter, F., Hoos, H., and Leyton-Brown, K. (2015) · 2015
Earlier work this paper cites.
Non-stochastic best arm identification and hyperparameter optimization
Jamieson, K. and Talwalkar, A. (2016) · 2016
Earlier work this paper cites.
Multi-fidelity Bayesian optimisation with continuous approximations
Kandasamy, K., Dasarathy, G., Schneider, J., and Póczos, B. (2017) · 2017
Earlier work this paper cites.
HyperBand: A novel bandit-based approach to hyperparameter optimization
Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., and Talwalkar, A. (2017) · 2017
Earlier work this paper cites.
Bayesian optimization in AlphaGo
Chen, Y., Huang, A., Wang, Z., Antonoglou, I., Schrittwieser, J., Silver, D., and de Freitas, N. (2018) · 2018
Earlier work this paper cites.
BOHB: Robust and efficient hyperparameter optimization at scale
Falkner, S., Klein, A., and Hutter, F. (2018) · 2018
Earlier work this paper cites.
Deep reinforcement learning that matters
Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., and Meger, D. (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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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
Auto-PyTorch: Multi-fidelity metalearning for efficient and robust AutoDL
Zimmer, L., Lindauer, M., and Hutter, F. (2021) · 2021
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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
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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
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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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DEHB: Evolutionary HyperBand for scalable, robust and efficient hyperparameter optimization
Awad, N., Mallik, N., and Hutter, F. (2021) · 2021
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HPOBench: A collection of reproducible multi-fidelity benchmark problems for HPO
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
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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
Later among the works it cites.
Watanabe, S. (2023) · 2023
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