| Snorkel: Rapid training data creation with weak supervision A Ratner, SH Bach, H Ehrenberg, J Fries, S Wu, C Ré The VLDB Journal 29 (2), 709-730, 2020 | 996 | 2020 |
| Interpretable decision sets: A joint framework for description and prediction H Lakkaraju, SH Bach, J Leskovec ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2016 | 741 | 2016 |
| Multitask prompted training enables zero-shot task generalization V Sanh, A Webson, C Raffel, SH Bach, L Sutawika, Z Alyafeai, A Chaffin, ... arXiv preprint arXiv:2110.08207, 2021 | 448 | 2021 |
| Hinge-loss Markov random fields and probabilistic soft logic SH Bach, M Broecheler, B Huang, L Getoor Journal of Machine Learning Research 18 (109), 1-67, 2017 | 436 | 2017 |
| A short introduction to probabilistic soft logic A Kimmig, SH Bach, M Broecheler, B Huang, L Getoor Proceedings of the NIPS Workshop on Probabilistic Programming: Foundations …, 2012 | 298 | 2012 |
| Paired learners for concept drift SH Bach, M Maloof IEEE International Conference on Data Mining (ICDM), 2008 | 182 | 2008 |
| Bloom: A 176b-parameter open-access multilingual language model TL Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, R Castagné, ... arXiv preprint arXiv:2211.05100, 2022 | 179 | 2022 |
| Learning the structure of generative models without labeled data SH Bach, B He, A Ratner, C Ré International Conference on Machine Learning (ICML), 2017 | 145 | 2017 |
| Hinge-loss Markov random fields: Convex inference for structured prediction SH Bach, B Huang, B London, L Getoor Uncertainty in Artificial Intelligence (UAI), 2013 | 141 | 2013 |
| 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, ... International Conference on Management of Data (SIGMOD), 2019 | 132 | 2019 |
| Snorkel: Fast training set generation for information extraction AJ Ratner, SH Bach, HR Ehrenberg, C Ré International Conference on Management of Data (SIGMOD) Demo, 2017 | 90 | 2017 |
| Promptsource: An integrated development environment and repository for natural language prompts SH Bach, V Sanh, ZX Yong, A Webson, C Raffel, NV Nayak, A Sharma, ... arXiv preprint arXiv:2202.01279, 2022 | 85 | 2022 |
| Weakly Supervised Sequence Tagging from Noisy Rules E Safranchik, S Luo, SH Bach AAAI Conference on Artificial Intelligence (AAAI), 2020 | 77 | 2020 |
| Scaling MPE inference for constrained continuous Markov random fields with consensus optimization SH Bach, M Broecheler, L Getoor, D O'Leary Advances in Neural Information Processing Systems (NIPS), 2012 | 68 | 2012 |
| A Bayesian approach to concept drift S Bach, M Maloof Advances in Neural Information Processing Systems (NIPS), 2010 | 38 | 2010 |
| Soft quantification in statistical relational learning G Farnadi, SH Bach, MF Moens, L Getoor, M De Cock Machine Learning 106, 1971-1991, 2017 | 35 | 2017 |
| Collective activity detection using hinge-loss Markov random fields B London, S Khamis, S Bach, B Huang, L Getoor, L Davis Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2013 | 28 | 2013 |
| Zero-shot learning with common sense knowledge graphs NV Nayak, SH Bach arXiv preprint arXiv:2006.10713, 2020 | 24 | 2020 |
| Social group modeling with probabilistic soft logic B Huang, SH Bach, E Norris, J Pujara, L Getoor NIPS Workshop on Social Network and Social Media Analysis: Methods, Models …, 2012 | 23 | 2012 |
| Graph Summarization in Annotated Data Using Probabilistic Soft Logic. A Memory, A Kimmig, SH Bach, L Raschid, L Getoor URSW, 75-86, 2012 | 21 | 2012 |
Lise GetoorProfessor of Computer Science, University of California, Santa CruzVerified email at ucsc.edu
