| Learning time-series shapelets J Grabocka, N Schilling, M Wistuba, L Schmidt-Thieme Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014 | 418 | 2014 |
| Ultra-fast shapelets for time series classification M Wistuba, J Grabocka, L Schmidt-Thieme arXiv preprint arXiv:1503.05018, 2015 | 86 | 2015 |
| 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 | 81* | 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 | 61 | 2017 |
| 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 | 44* | 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 | 40 | 2016 |
| 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 | 32 | 2020 |
| Learning surrogate losses J Grabocka, R Scholz, L Schmidt-Thieme arXiv preprint arXiv:1905.10108, 2019 | 32 | 2019 |
| Latent time-series motifs J Grabocka, N Schilling, L Schmidt-Thieme ACM Transactions on Knowledge Discovery from Data (TKDD) 11 (1), 1-20, 2016 | 32 | 2016 |
| Dataset2vec: Learning dataset meta-features HS Jomaa, L Schmidt-Thieme, J Grabocka Data Mining and Knowledge Discovery 35 (3), 964-985, 2021 | 31 | 2021 |
| Hyp-rl: Hyperparameter optimization by reinforcement learning HS Jomaa, J Grabocka, L Schmidt-Thieme arXiv preprint arXiv:1906.11527, 2019 | 30 | 2019 |
| 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 | 23 | 2012 |
| 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 | 22 | 2019 |
| Classification of sparse time series via supervised matrix factorization J Grabocka, A Nanopoulos, L Schmidt-Thieme Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012 | 22 | 2012 |
| Few-shot Bayesian optimization with deep kernel surrogates M Wistuba, J Grabocka Proceedings of the Ninth International Conference on Learning …, 2021 | 21 | 2021 |
| Neuralwarp: Time-series similarity with warping networks J Grabocka, L Schmidt-Thieme arXiv preprint arXiv:1812.08306, 2018 | 17 | 2018 |
| Invariant time-series factorization J Grabocka, L Schmidt-Thieme Data mining and knowledge discovery 28 (5), 1455-1479, 2014 | 17 | 2014 |
| Scalable Pareto Front Approximation for Deep Multi-Objective Learning M Ruchte, J Grabocka 2021 IEEE International Conference on Data Mining (ICDM), 1306-1311, 2021 | 16* | 2021 |
| 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 | 11* | 2021 |
Lars Schmidt-ThiemeUniversity of Hildesheim, GermanyVerified email at ismll.uni-hildesheim.de
