Alex Ratner
Alex Ratner
Assistant Professor, University of Washington
Verified email at - Homepage
Cited by
Cited by
Snorkel: Rapid training data creation with weak supervision
A Ratner, SH Bach, H Ehrenberg, J Fries, S Wu, C Ré
Proceedings of the VLDB Endowment. International Conference on Very Large …, 2017
Data programming: Creating large training sets, quickly
AJ Ratner, CM De Sa, S Wu, D Selsam, C Ré
Advances in neural information processing systems 29, 2016
Learning to compose domain-specific transformations for data augmentation
AJ Ratner, H Ehrenberg, Z Hussain, J Dunnmon, C Ré
Advances in neural information processing systems 30, 2017
Deepdive: Declarative knowledge base construction
C De Sa, A Ratner, C Ré, J Shin, F Wang, S Wu, C Zhang
ACM SIGMOD Record 45 (1), 60-67, 2016
Training complex models with multi-task weak supervision
A Ratner, B Hancock, J Dunnmon, F Sala, S Pandey, C Ré
Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 4763-4771, 2019
A kernel theory of modern data augmentation
T Dao, A Gu, A Ratner, V Smith, C De Sa, C Ré
International Conference on Machine Learning, 1528-1537, 2019
Learning the structure of generative models without labeled data
SH Bach, B He, A Ratner, C Ré
International Conference on Machine Learning, 273-282, 2017
Snorkel drybell: A case study in deploying weak supervision at industrial scale
SH Bach, D Rodriguez, Y Liu, C Luo, H Shao, C Xia, S Sen, A Ratner, ...
Proceedings of the 2019 International Conference on Management of Data, 362-375, 2019
Swellshark: A generative model for biomedical named entity recognition without labeled data
J Fries, S Wu, A Ratner, C Ré
arXiv preprint arXiv:1704.06360, 2017
AMELIE speeds Mendelian diagnosis by matching patient phenotype and genotype to primary literature
J Birgmeier, M Haeussler, CA Deisseroth, EH Steinberg, KA Jagadeesh, ...
Science Translational Medicine 12 (544), eaau9113, 2020
Weak supervision: the new programming paradigm for machine learning
A Ratner, S Bach, P Varma, C Ré
Hazy Research. Available via https://dawn. cs. stanford. edu//2017/07/16 …, 2019
Learning dependency structures for weak supervision models
P Varma, F Sala, A He, A Ratner, C Ré
International Conference on Machine Learning, 6418-6427, 2019
Wrench: A comprehensive benchmark for weak supervision
J Zhang, Y Yu, Y Li, Y Wang, Y Yang, M Yang, A Ratner
arXiv preprint arXiv:2109.11377, 2021
Snorkel metal: Weak supervision for multi-task learning
A Ratner, B Hancock, J Dunnmon, R Goldman, C Ré
Proceedings of the Second Workshop on Data Management for End-To-End Machine …, 2018
Slice-based learning: A programming model for residual learning in critical data slices
V Chen, S Wu, AJ Ratner, J Weng, C Ré
Advances in neural information processing systems 32, 2019
Cross-modal data programming enables rapid medical machine learning
JA Dunnmon, AJ Ratner, K Saab, N Khandwala, M Markert, H Sagreiya, ...
Patterns 1 (2), 100019, 2020
A machine-compiled database of genome-wide association studies
V Kuleshov, J Ding, C Vo, B Hancock, A Ratner, Y Li, C Ré, S Batzoglou, ...
Nature communications 10 (1), 3341, 2019
Data programming with ddlite: Putting humans in a different part of the loop
HR Ehrenberg, J Shin, AJ Ratner, JA Fries, C Ré
Proceedings of the Workshop on Human-In-the-Loop Data Analytics, 1-6, 2016
A survey on programmatic weak supervision
J Zhang, CY Hsieh, Y Yu, C Zhang, A Ratner
arXiv preprint arXiv:2202.05433, 2022
The Role of Massively Multi-Task and Weak Supervision in Software 2.0.
AJ Ratner, B Hancock, C Ré
CIDR, 2019
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