| 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 | 548 | 2014 |
| Well-tuned Simple Nets Excel on Tabular Datasets A Kadra, M Lindauer, F Hutter, J Grabocka Neural Information Processing Systems (NeurIPS 2021), 2021 | 143* | 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 | 95* | 2016 |
| Ultra-fast shapelets for time series classification M Wistuba, J Grabocka, L Schmidt-Thieme arXiv preprint arXiv:1503.05018, 2015 | 95 | 2015 |
| 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 | 79 | 2017 |
| Hyp-rl: Hyperparameter optimization by reinforcement learning HS Jomaa, J Grabocka, L Schmidt-Thieme arXiv preprint arXiv:1906.11527, 2019 | 66 | 2019 |
| 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 | 58 | 2020 |
| 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 | 55 | 2016 |
| Dataset2vec: Learning dataset meta-features HS Jomaa, L Schmidt-Thieme, J Grabocka Data Mining and Knowledge Discovery 35 (3), 964-985, 2021 | 53 | 2021 |
| Few-shot Bayesian optimization with deep kernel surrogates M Wistuba, J Grabocka International Conference on Learning Representations (ICLR 2021), 2021 | 50 | 2021 |
| Scalable Pareto Front Approximation for Deep Multi-Objective Learning M Ruchte, J Grabocka IEEE International Conference on Data Mining (ICDM 2021), 1306-1311, 2021 | 43* | 2021 |
| Learning surrogate losses J Grabocka, R Scholz, L Schmidt-Thieme arXiv preprint arXiv:1905.10108, 2019 | 43 | 2019 |
| Attribute-aware non-linear co-embeddings of graph features A Rashed, J Grabocka, L Schmidt-Thieme ACM Recommender Systems (RecSys), 314-321, 2019 | 38 | 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 | 37 | 2016 |
| Transformers Can Do Bayesian Inference S Müller, N Hollmann, SP Arango, J Grabocka, F Hutter International Conference on Learning Representations (ICLR 2022), 2021 | 29 | 2021 |
| 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 | 29* | 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 |
| Invariant time-series classification J Grabocka, A Nanopoulos, L Schmidt-Thieme Machine Learning and Knowledge Discovery in Databases: European Conference …, 2012 | 24 | 2012 |
| Neuralwarp: Time-series similarity with warping networks J Grabocka, L Schmidt-Thieme arXiv preprint arXiv:1812.08306, 2018 | 23 | 2018 |
| Classification of sparse time series via supervised matrix factorization J Grabocka, A Nanopoulos, L Schmidt-Thieme AAAI Conference on Artificial Intelligence (AAAI 2012), 2012 | 23 | 2012 |
Lars Schmidt-ThiemeUniversity of Hildesheim, GermanyVerified email at ismll.uni-hildesheim.de
