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Prof. Dr. Josif Grabocka
Prof. Dr. Josif Grabocka
W1 Professor of Representation Learning, University of Freiburg
Verified email at informatik.uni-freiburg.de
Title
Cited by
Cited by
Year
Learning time-series shapelets
J Grabocka, N Schilling, M Wistuba, L Schmidt-Thieme
Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014
4302014
Ultra-fast shapelets for time series classification
M Wistuba, J Grabocka, L Schmidt-Thieme
arXiv preprint arXiv:1503.05018, 2015
862015
Fast classification of univariate and multivariate time series through shapelet discovery
J Grabocka, M Wistuba, L Schmidt-Thieme
Knowledge and information systems 49 (2), 429-454, 2016
83*2016
Personalized deep learning for tag recommendation
HTH Nguyen, M Wistuba, J Grabocka, LR Drumond, L Schmidt-Thieme
Pacific-Asia Conference on Knowledge Discovery and Data Mining, 186-197, 2017
612017
Well-tuned Simple Nets Excel on Tabular Datasets
A Kadra, M Lindauer, F Hutter, J Grabocka
Proceedings of the 35th Conference on Neural Information Processing Systems …, 2021
45*2021
Learning DTW-shapelets for time-series classification
M Shah, J Grabocka, N Schilling, M Wistuba, L Schmidt-Thieme
Proceedings of the 3rd IKDD Conference on Data Science, 2016, 1-8, 2016
412016
Self-supervised learning for semi-supervised time series classification
S Jawed, J Grabocka, L Schmidt-Thieme
Pacific-Asia Conference on Knowledge Discovery and Data Mining, 499-511, 2020
362020
Dataset2vec: Learning dataset meta-features
HS Jomaa, L Schmidt-Thieme, J Grabocka
Data Mining and Knowledge Discovery 35 (3), 964-985, 2021
352021
Hyp-rl: Hyperparameter optimization by reinforcement learning
HS Jomaa, J Grabocka, L Schmidt-Thieme
arXiv preprint arXiv:1906.11527, 2019
342019
Learning surrogate losses
J Grabocka, R Scholz, L Schmidt-Thieme
arXiv preprint arXiv:1905.10108, 2019
332019
Latent time-series motifs
J Grabocka, N Schilling, L Schmidt-Thieme
ACM Transactions on Knowledge Discovery from Data (TKDD) 11 (1), 1-20, 2016
332016
Attribute-aware non-linear co-embeddings of graph features
A Rashed, J Grabocka, L Schmidt-Thieme
Proceedings of the 13th ACM conference on recommender systems, 314-321, 2019
252019
Scalable classification of repetitive time series through frequencies of local polynomials
J Grabocka, M Wistuba, L Schmidt-Thieme
IEEE Transactions on Knowledge and Data Engineering 27 (6), 1683-1695, 2014
24*2014
Invariant time-series classification
J Grabocka, A Nanopoulos, L Schmidt-Thieme
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2012
232012
Classification of sparse time series via supervised matrix factorization
J Grabocka, A Nanopoulos, L Schmidt-Thieme
Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012
222012
Few-shot Bayesian optimization with deep kernel surrogates
M Wistuba, J Grabocka
Proceedings of the Ninth International Conference on Learning …, 2021
212021
Invariant time-series factorization
J Grabocka, L Schmidt-Thieme
Data mining and knowledge discovery 28 (5), 1455-1479, 2014
182014
Scalable Pareto Front Approximation for Deep Multi-Objective Learning
M Ruchte, J Grabocka
2021 IEEE International Conference on Data Mining (ICDM), 1306-1311, 2021
17*2021
Neuralwarp: Time-series similarity with warping networks
J Grabocka, L Schmidt-Thieme
arXiv preprint arXiv:1812.08306, 2018
172018
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML
SP Arango, HS Jomaa, M Wistuba, J Grabocka
Proceedings of the 35th Conference on Neural Information Processing Systems …, 2021
12*2021
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