Tammo Rukat
Tammo Rukat
Amazon Research
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DataWig: Missing Value Imputation for Tables.
F Biessmann, T Rukat, P Schmidt, P Naidu, S Schelter, A Taptunov, ...
J. Mach. Learn. Res. 20 (175), 1-6, 2019
Bayesian Boolean matrix factorisation
T Rukat, CC Holmes, MK Titsias, C Yau
International conference on machine learning, 2969-2978, 2017
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
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
Probabilistic boolean tensor decomposition
T Rukat, C Holmes, C Yau
International conference on machine learning, 4413-4422, 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
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
Automated data validation in machine learning systems
F Biessmann, J Golebiowski, T Rukat, D Lange, P Schmidt
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
JENGA: a framework to study the impact of data errors on the predictions of machine learning models
S Schelter, T Rukat, F Biessmann
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
Deequ-data quality validation for machine learning pipelines
S Schelter, P Schmidt, T Rukat, M Kiessling, A Taptunov, F Biessmann, ...
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
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
Towards automated ML model monitoring: Measure, improve and quantify data quality
T Rukat, D Lange, S Schelter, F Biessmann
An interpretable latent variable model for attribute applicability in the amazon catalogue
T Rukat, D Lange, C Archambeau
arXiv preprint arXiv:1712.00126, 2017
Tensormachine: probabilistic Boolean tensor decomposition
T Rukat, CC Holmes, C Yau
arXiv preprint arXiv:1805.04582, 2018
Variational boosted soft trees
T Cinquin, T Rukat, P Schmidt, M Wistuba, A Bekasov
arXiv preprint arXiv:2302.10706, 2023
Bayesian Nonparametric Boolean Factor Models
T Rukat, C Yau
arXiv preprint arXiv:1907.00063, 2019
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