fix: add hpob and tabrepo to readme (#1)
Browse files- fix: add hpob and tabrepo to readme (1676dc5a2d56e4919d57d381b6900b5171f27639)
Co-authored-by: Luca Thale-Bombien <[email protected]>
README.md
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@@ -8,6 +8,8 @@ This dataset contains hyperparameter optimization (HPO) evaluations from several
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- nasbench201: NAS-Bench-201: Extending the scope of reproducible neural architecture search. Dong, X. and Yang, Y. 2020.
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- pd1: Pre-trained Gaussian processes for Bayesian optimization. Wang, Z. and Dahl G. and Swersky K. and Lee C. and Mariet Z. and Nado Z. and Gilmer J. and Snoek J. and Ghahramani Z. 2021.
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- yahpo: YAHPO Gym - An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization. Pfisterer F., Schneider S., Moosbauer J., Binder M., Bischl B., 2022
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The evaluations can be accessed through [Syne Tune](https://github.com/syne-tune/syne-tune) HPO library by calling the following:
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- nasbench201: NAS-Bench-201: Extending the scope of reproducible neural architecture search. Dong, X. and Yang, Y. 2020.
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- pd1: Pre-trained Gaussian processes for Bayesian optimization. Wang, Z. and Dahl G. and Swersky K. and Lee C. and Mariet Z. and Nado Z. and Gilmer J. and Snoek J. and Ghahramani Z. 2021.
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- yahpo: YAHPO Gym - An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization. Pfisterer F., Schneider S., Moosbauer J., Binder M., Bischl B., 2022
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- tabrepo: TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications. Salinas D., Erickson N., 2024.
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- hpob: HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML. Arango S., Jomaa H., Wistuba M., Grabocka J., 2021.
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The evaluations can be accessed through [Syne Tune](https://github.com/syne-tune/syne-tune) HPO library by calling the following:
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