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Efficient bayesian hyperparameter optimization. University of Freiburg, Germany, (2020)base-search.net (ftunivfreiburg:oai:freidok.uni-freiburg.de:166637).Learning Curve Prediction with Bayesian Neural Networks., , , and . ICLR (Poster), OpenReview.net, (2017)HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO, , , , , , , , and . Proceedings of the international conference on Neural Information Processing Systems (NeurIPS) (Datasets and Benchmarks Track), (December 2021)Optimizing Hyperparameters with Conformal Quantile Regression., , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 29876-29893. PMLR, (2023)Efficient and Robust Automated Machine Learning., , , , , and . NIPS, page 2962-2970. (2015)Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research., , , , , and . AutoML, volume 188 of Proceedings of Machine Learning Research, page 16/1-23. PMLR, (2022)BOHB: Robust and Efficient Hyperparameter Optimization at Scale., , and . ICML, volume 80 of Proceedings of Machine Learning Research, page 1436-1445. PMLR, (2018)Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization., and . CoRR, (2019)Automatic Termination for Hyperparameter Optimization., , , , , , , and . AutoML, volume 188 of Proceedings of Machine Learning Research, page 7/1-21. PMLR, (2022)NAS-Bench-101: Towards Reproducible Neural Architecture Search., , , , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 7105-7114. PMLR, (2019)