| 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 | 1192 | 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 | 740 | 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 | 363 | 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 | 224* | 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 | 190 | 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 | 175 | 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 | 161 | 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 | 144 | 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 | 102 | 2017 |
| 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 | 84 | 2021 |
| 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 | 72* | 2020 |
| 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 | 67 | 2019 |
| 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 | 61 | 2018 |
| 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 | 60 | 2019 |
| Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes CY Hsieh, CL Li, CK Yeh, H Nakhost, Y Fujii, A Ratner, R Krishna, CY Lee, ... arXiv preprint arXiv:2305.02301, 2023 | 57 | 2023 |
| A survey on programmatic weak supervision J Zhang, CY Hsieh, Y Yu, C Zhang, A Ratner arXiv preprint arXiv:2202.05433, 2022 | 54 | 2022 |
| DataComp: In search of the next generation of multimodal datasets SY Gadre, G Ilharco, A Fang, J Hayase, G Smyrnis, T Nguyen, R Marten, ... arXiv preprint arXiv:2304.14108, 2023 | 46 | 2023 |
| Cross-modal data programming enables rapid medical machine learning JA Dunnmon, AJ Ratner, K Saab, N Khandwala, M Markert, H Sagreiya, ... Patterns 1 (2), 2020 | 46 | 2020 |
| 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 | 46 | 2019 |
| 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 | 36 | 2019 |
Christopher RéComputer Science, Stanford UniversityVerified email at cs.stanford.edu
