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Josif Grabocka
Josif Grabocka
Professor of Machine Learning
Verified email at utn.de - Homepage
Title
Cited by
Cited by
Year
Learning time-series shapelets
J Grabocka, N Schilling, M Wistuba, L Schmidt-Thieme
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2014), 392-401, 2014
5682014
Well-tuned Simple Nets Excel on Tabular Datasets
A Kadra, M Lindauer, F Hutter, J Grabocka
Neural Information Processing Systems (NeurIPS 2021), 2021
163*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
97*2016
Ultra-fast shapelets for time series classification
M Wistuba, J Grabocka, L Schmidt-Thieme
arXiv preprint arXiv:1503.05018, 2015
962015
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
842017
Hyp-rl: Hyperparameter optimization by reinforcement learning
HS Jomaa, J Grabocka, L Schmidt-Thieme
arXiv preprint arXiv:1906.11527, 2019
662019
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
612020
Dataset2vec: Learning dataset meta-features
HS Jomaa, L Schmidt-Thieme, J Grabocka
Data Mining and Knowledge Discovery 35 (3), 964-985, 2021
572021
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
552016
Few-shot Bayesian optimization with deep kernel surrogates
M Wistuba, J Grabocka
International Conference on Learning Representations (ICLR 2021), 2021
532021
Scalable Pareto Front Approximation for Deep Multi-Objective Learning
M Ruchte, J Grabocka
IEEE International Conference on Data Mining (ICDM 2021), 1306-1311, 2021
50*2021
Transformers Can Do Bayesian Inference
S Müller, N Hollmann, SP Arango, J Grabocka, F Hutter
International Conference on Learning Representations (ICLR 2022), 2021
462021
Learning surrogate losses
J Grabocka, R Scholz, L Schmidt-Thieme
arXiv preprint arXiv:1905.10108, 2019
432019
Attribute-aware non-linear co-embeddings of graph features
A Rashed, J Grabocka, L Schmidt-Thieme
ACM Recommender Systems (RecSys), 314-321, 2019
382019
Latent time-series motifs
J Grabocka, N Schilling, L Schmidt-Thieme
ACM Transactions on Knowledge Discovery from Data (TKDD) 11 (1), 1-20, 2016
372016
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
32*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
27*2014
Neuralwarp: Time-series similarity with warping networks
J Grabocka, L Schmidt-Thieme
arXiv preprint arXiv:1812.08306, 2018
242018
Invariant time-series classification
J Grabocka, A Nanopoulos, L Schmidt-Thieme
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2012
242012
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
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