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Öztürk E, Ferreira F, Jomaa H, Schmidt-Thieme L, Grabocka J, and Hutter F. Zero-shot AutoML with pretrained models. In: Proceedings of the 39th International Conference on Machine Learning (ICML’22). Ed. by Chaudhuri K, Jegelka S, Song L, Szepesvari C, Niu G, and Sabato S. Vol. 162. PMLR, 2022:17138–17155.
Wistuba M, Kadra A, and Grabocka J. Supervising the multi-fidelity race of hyperparameter configurations. In: Advances in Neural Information Processing Systems (NeurIPS’22). Ed. by Oh AH, Agarwal A, Belgrave D, and Cho K. 2022. URL: https://openreview.net/forum?id=0Fe7bAWmJr.
Jomaa HS, Schmidt-Thieme L, and Grabocka J. Dataset2Vec: Learning dataset meta-features. Data Mining and Knowledge Discovery 2021;35:964–985.
Kadra A, Lindauer M, Hutter F, and Grabocka J. Well-tuned simple nets excel on tabular datasets. In: Advances in Neural Information Processing Systems 34 (NeurIPS’21). Ed. by Ranzato M, Beygelzimer A, Dauphin Y, Liang P, and Vaughan JW. 2021:23928–23941.
Pineda Arango S, Jomaa H, Wistuba M, and Grabocka J. HPO-B: A large-scale reproducible benchmark for blackbox HPO based on OpenML. In: Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks’21). Ed. by Vanschoren J and Yeung S. 2021. URL: https://openreview.net/forum?id=O24OhmqpJtP.
Rashed A, Grabocka J, and Schmidt-Thieme L. A guided learning approach for item recommendation via surrogate loss learning. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’21). ACM, 2021:605–613.
Ruchte M and Grabocka J. Scalable Pareto front approximation for deep multi-objective learning. In: 2021 IEEE International Conference on Data Mining (ICDM’21). 2021:1306–1311.
Wistuba M and Grabocka J. Few-shot Bayesian optimization with deep kernel surrogates. In: International Conference on Learning Representations (ICLR’21). 2021. URL: https://openreview.net/forum?id=bJxgv5C3sYc.
Grabocka J, Schilling N, Wistuba M, and Schmidt-Thieme L. Learning time-series shapelets. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’14). ACM, 2014:392–401.
Institute of Medical Biometry and Statistics,
Faculty of Medicine and Medical Center –
University of Freiburg
