| DataWig: Missing value imputation for tables F Biessmann, T Rukat, P Schmidt, P Naidu, S Schelter, A Taptunov, ... Journal of Machine Learning Research 20 (175), 1-6, 2019 | 109 | 2019 |
| Learning to validate the predictions of black box classifiers on unseen data S Schelter, T Rukat, F Bießmann Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020 | 45 | 2020 |
| Bayesian boolean matrix factorisation T Rukat, CC Holmes, MK Titsias, C Yau International conference on machine learning, 2969-2978, 2017 | 39 | 2017 |
| Automated data validation in machine learning systems F Biessmann, J Golebiowski, T Rukat, D Lange, P Schmidt | 26 | 2021 |
| JENGA: a framework to study the impact of data errors on the predictions of machine learning models S Schelter, T Rukat, F Biessmann | 24 | 2021 |
| Chain-length dependent growth dynamics of n-alkanes on silica investigated by energy-dispersive x-ray reflectivity in situ and in real-time C Weber, C Frank, S Bommel, T Rukat, W Leitenberger, P Schäfer, ... The Journal of Chemical Physics 136 (20), 2012 | 24 | 2012 |
| Unit testing data with deequ S Schelter, F Biessmann, D Lange, T Rukat, P Schmidt, S Seufert, ... Proceedings of the 2019 International Conference on Management of Data, 1993 …, 2019 | 20 | 2019 |
| Deequ-data quality validation for machine learning pipelines S Schelter, P Schmidt, T Rukat, M Kiessling, A Taptunov, F Biessmann, ... | 20 | 2018 |
| Learning to validate the predictions of black box machine learning models on unseen data S Redyuk, S Schelter, T Rukat, V Markl, F Biessmann Proceedings of the Workshop on Human-In-the-Loop Data Analytics, 1-4, 2019 | 19 | 2019 |
| Differential data quality verification on partitioned data S Schelter, S Grafberger, P Schmidt, T Rukat, M Kiessling, A Taptunov, ... 2019 IEEE 35th International Conference on Data Engineering (ICDE), 1940-1945, 2019 | 19 | 2019 |
| Probabilistic boolean tensor decomposition T Rukat, C Holmes, C Yau International conference on machine learning, 4413-4422, 2018 | 19 | 2018 |
| Resting state brain networks from EEG: Hidden Markov states vs. classical microstates T Rukat, A Baker, A Quinn, M Woolrich arXiv preprint arXiv:1606.02344, 2016 | 19 | 2016 |
| Towards automated data quality management for machine learning T Rukat, D Lange, S Schelter, F Biessmann ML Ops Work. Conf. Mach. Learn. Syst, 1-3, 2020 | 12 | 2020 |
| Ten simple rules for surviving an interdisciplinary PhD S Demharter, N Pearce, K Beattie, I Frost, J Leem, A Martin, ... PLoS Computational Biology 13 (5), e1005512, 2017 | 11 | 2017 |
| Towards automated ml model monitoring: Measure, improve and quantify data quality T Rukat, D Lange, S Schelter, F Biessmann | 8 | 2020 |
| Dynamic contrast‐enhanced MRI in mice: An investigation of model parameter uncertainties T Rukat, S Walker‐Samuel, SA Reinsberg Magnetic Resonance in Medicine 73 (5), 1979-1987, 2015 | 8 | 2015 |
| An interpretable latent variable model for attribute applicability in the amazon catalogue T Rukat, D Lange, C Archambeau arXiv preprint arXiv:1712.00126, 2017 | 5 | 2017 |
| Tensormachine: probabilistic Boolean tensor decomposition T Rukat, CC Holmes, C Yau arXiv preprint arXiv:1805.04582, 2018 | 2 | 2018 |
| Variational boosted soft trees T Cinquin, T Rukat, P Schmidt, M Wistuba, A Bekasov International Conference on Artificial Intelligence and Statistics, 5787-5801, 2023 | 1 | 2023 |
| Bayesian Nonparametric Boolean Factor Models T Rukat, C Yau arXiv preprint arXiv:1907.00063, 2019 | | 2019 |
Chris Holmes在 stats.ox.ac.uk 的电子邮件经过验证
