Stephen H. Bach
Stephen H. Bach
Assistant Professor of Computer Science, Brown University
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Cited by
Cited by
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
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
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
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
Paired learners for concept drift
SH Bach, M Maloof
IEEE International Conference on Data Mining (ICDM), 2008
Hinge-loss Markov random fields: Convex inference for structured prediction
SH Bach, B Huang, B London, L Getoor
Uncertainty in Artificial Intelligence (UAI), 2013
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
Learning the structure of generative models without labeled data
SH Bach, B He, A Ratner, C Ré
International Conference on Machine Learning (ICML), 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, ...
International Conference on Management of Data (SIGMOD), 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
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
Weakly Supervised Sequence Tagging from Noisy Rules
E Safranchik, S Luo, SH Bach
AAAI Conference on Artificial Intelligence (AAAI), 2020
A Bayesian approach to concept drift
S Bach, M Maloof
Advances in Neural Information Processing Systems (NIPS), 2010
Soft quantification in statistical relational learning
G Farnadi, SH Bach, MF Moens, L Getoor, M De Cock
Machine Learning 106 (12), 1971-1991, 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
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
Graph Summarization in Annotated Data Using Probabilistic Soft Logic.
A Memory, A Kimmig, SH Bach, L Raschid, L Getoor
URSW, 75-86, 2012
Adversarial Multiclass Learning under Weak Supervision with Performance Guarantees
A Mazzetto, C Cousins, D Sam, SH Bach, E Upfal
International Conference on Machine Learning (ICML), 2021
Semi-supervised aggregation of dependent weak supervision sources with performance guarantees
A Mazzetto, D Sam, A Park, E Upfal, SH Bach
Artificial Intelligence and Statistics (AISTATS), 2021
Paired-dual learning for fast training of latent variable hinge-loss MRFs
S Bach, B Huang, J Boyd-Graber, L Getoor
International Conference on Machine Learning (ICML), 2015
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