| Bayesian boolean matrix factorisation T Rukat, CC Holmes, MK Titsias, C Yau International conference on machine learning, 2969-2978, 2017 | 26 | 2017 |
| 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), 204709, 2012 | 22 | 2012 |
| 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 | 14 | 2019 |
| Probabilistic boolean tensor decomposition T Rukat, C Holmes, C Yau International conference on machine learning, 4413-4422, 2018 | 12 | 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 | 10 | 2016 |
| 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 | 9 | 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 | 8 | 2020 |
| 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 | 7 | 2019 |
| 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 | 7 | 2015 |
| 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 | 5 | 2019 |
| 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 | 4 | 2017 |
| An interpretable latent variable model for attribute applicability in the amazon catalogue T Rukat, D Lange, C Archambeau arXiv preprint arXiv:1712.00126, 2017 | 3 | 2017 |
| Towards Automated ML Model Monitoring: Measure, Improve and Quantify Data Quality T Rukat, D Lange, S Schelter, F Biessmann ML Ops workshop at MLSys, 2019 | 2 | 2019 |
| Deequ-Data Quality Validation for Machine Learning Pipelines S Schelter, S Grafberger, P Schmidt, T Rukat, M Kiessling, A Taptunov, ... | 2 | 2018 |
| Towards Automated Data Quality Management for Machine Learning T Rukat, D Lange, S Schelter, F Biessmann ML Ops workshop at the Conference on ML and Systems (MLSys), 2020 | 1 | 2020 |
| Tensormachine: probabilistic Boolean tensor decomposition T Rukat, CC Holmes, C Yau arXiv preprint arXiv:1805.04582, 2018 | 1 | 2018 |
| JENGA-A Framework to Study the Impact of Data Errors on the Predictions of Machine Learning Models S Schelter, T Rukat, F Biessmann | | 2021 |
| Bayesian Nonparametric Boolean Factor Models T Rukat, C Yau arXiv preprint arXiv:1907.00063, 2019 | | 2019 |
| Logical factorisation machines: probabilistic boolean factor models for binary data T Rukat University of Oxford, 2018 | | 2018 |
| Automated Data Validation in Machine Learning Systems F Biessmann, J Golebiowski, T Rukat, D Lange, P Schmidt | | 2015 |
Chris HolmesVerified email at stats.ox.ac.uk
