Tammo Rukat
Tammo Rukat
Verified email at dtc.ox.ac.uk - Homepage
Cited by
Cited by
Bayesian boolean matrix factorisation
T Rukat, CC Holmes, MK Titsias, C Yau
International conference on machine learning, 2969-2978, 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
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
Probabilistic boolean tensor decomposition
T Rukat, C Holmes, C Yau
International conference on machine learning, 4413-4422, 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
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
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
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
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
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
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
An interpretable latent variable model for attribute applicability in the amazon catalogue
T Rukat, D Lange, C Archambeau
arXiv preprint arXiv:1712.00126, 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
Deequ-Data Quality Validation for Machine Learning Pipelines
S Schelter, S Grafberger, P Schmidt, T Rukat, M Kiessling, A Taptunov, ...
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
Tensormachine: probabilistic Boolean tensor decomposition
T Rukat, CC Holmes, C Yau
arXiv preprint arXiv:1805.04582, 2018
JENGA-A Framework to Study the Impact of Data Errors on the Predictions of Machine Learning Models
S Schelter, T Rukat, F Biessmann
Bayesian Nonparametric Boolean Factor Models
T Rukat, C Yau
arXiv preprint arXiv:1907.00063, 2019
Logical factorisation machines: probabilistic boolean factor models for binary data
T Rukat
University of Oxford, 2018
Automated Data Validation in Machine Learning Systems
F Biessmann, J Golebiowski, T Rukat, D Lange, P Schmidt
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