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Josif Grabocka
Josif Grabocka
Professor of Representation Learning (W1), University of Freiburg
Verified email at informatik.uni-freiburg.de - Homepage
Title
Cited by
Cited by
Year
Learning time-series shapelets
J Grabocka, N Schilling, M Wistuba, L Schmidt-Thieme
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining†…, 2014
4882014
Ultra-fast shapelets for time series classification
M Wistuba, J Grabocka, L Schmidt-Thieme
arXiv preprint arXiv:1503.05018, 2015
972015
Well-tuned Simple Nets Excel on Tabular Datasets
A Kadra, M Lindauer, F Hutter, J Grabocka
Neural Information Processing Systems (NeurIPS 2021), 2021
96*2021
Fast classification of univariate and multivariate time series through shapelet discovery
J Grabocka, M Wistuba, L Schmidt-Thieme
Knowledge and Information Systems (KAIS) 49 (2), 429-454, 2016
90*2016
Personalized deep learning for tag recommendation
HTH Nguyen, M Wistuba, J Grabocka, LR Drumond, L Schmidt-Thieme
Advances in Knowledge Discovery and Data Mining: 21st Pacific-Asia†…, 2017
732017
Hyp-rl: Hyperparameter optimization by reinforcement learning
HS Jomaa, J Grabocka, L Schmidt-Thieme
arXiv preprint arXiv:1906.11527, 2019
502019
Self-supervised learning for semi-supervised time series classification
S Jawed, J Grabocka, L Schmidt-Thieme
Advances in Knowledge Discovery and Data Mining: 24th Pacific-Asia†…, 2020
472020
Learning DTW-shapelets for time-series classification
M Shah, J Grabocka, N Schilling, M Wistuba, L Schmidt-Thieme
IKDD Conference on Data Science, 2016, 1-8, 2016
472016
Dataset2vec: Learning dataset meta-features
HS Jomaa, L Schmidt-Thieme, J Grabocka
Data Mining and Knowledge Discovery 35 (3), 964-985, 2021
442021
Learning surrogate losses
J Grabocka, R Scholz, L Schmidt-Thieme
arXiv preprint arXiv:1905.10108, 2019
392019
Latent time-series motifs
J Grabocka, N Schilling, L Schmidt-Thieme
ACM Transactions on Knowledge Discovery from Data (TKDD) 11 (1), 1-20, 2016
362016
Few-shot Bayesian optimization with deep kernel surrogates
M Wistuba, J Grabocka
International Conference on Learning Representations (ICLR 2021), 2021
352021
Attribute-aware non-linear co-embeddings of graph features
A Rashed, J Grabocka, L Schmidt-Thieme
ACM Recommender Systems (RecSys), 314-321, 2019
352019
Scalable Pareto Front Approximation for Deep Multi-Objective Learning
M Ruchte, J Grabocka
IEEE International Conference on Data Mining (ICDM 2021), 1306-1311, 2021
31*2021
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 (TKDE) 27 (6), 1683-1695, 2014
26*2014
Classification of sparse time series via supervised matrix factorization
J Grabocka, A Nanopoulos, L Schmidt-Thieme
AAAI Conference on Artificial Intelligence (AAAI 2012), 2012
232012
Invariant Time-Series Classification.
J Grabocka, A Nanopoulos, L Schmidt-Thieme
ECML/PKDD (2) 7524, 725-740, 2012
222012
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML
SP Arango, HS Jomaa, M Wistuba, J Grabocka
Neural Information Processing Systems (NeurIPS 2021), Datasets and†…, 2021
21*2021
Neuralwarp: Time-series similarity with warping networks
J Grabocka, L Schmidt-Thieme
arXiv preprint arXiv:1812.08306, 2018
202018
Invariant time-series factorization
J Grabocka, L Schmidt-Thieme
Data Mining and Knowledge Discovery 28 (5), 1455-1479, 2014
182014
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