Prof. Dr.  

Josif Grabocka

Department of Engineering, University of Technology Nuremberg
Ulmenstraße 52i, 90443 Nuremberg


Selected Publications

Müller S, Hollmann N, Arango SP, Grabocka J, and Hutter F. Transformers can do Bayesian inference. In: International Conference on Learning Representations (ICLR’22). 2022. URL:

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:

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:

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:

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.

Administrative Manager

Marc Schumacher

Institute of Medical Biometry and Statistics,
Faculty of Medicine and Medical Center –
University of Freiburg