Stephen H. Bach
Stephen H. Bach
Assistant Professor of Computer Science, Brown University
Verified email at cs.brown.edu - Homepage
Title
Cited by
Cited by
Year
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
3262016
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
2702017
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 11 (3), 269-282, 2017
2292017
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
2222012
Paired learners for concept drift
SH Bach, M Maloof
IEEE International Conference on Data Mining (ICDM), 2008
1282008
Hinge-loss Markov random fields: Convex inference for structured prediction
SH Bach, B Huang, B London, L Getoor
Uncertainty in Artificial Intelligence (UAI), 2013
1222013
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
632012
Learning the structure of generative models without labeled data
SH Bach, B He, A Ratner, C Ré
International Conference on Machine Learning (ICML), 2017
562017
Snorkel: Fast training set generation for information extraction
AJ Ratner, SH Bach, HR Ehrenberg, C Ré
Proceedings of the 2017 ACM international conference on management of data …, 2017
382017
A Bayesian approach to concept drift
S Bach, M Maloof
Advances in Neural Information Processing Systems (NIPS), 2010
302010
Soft quantification in statistical relational learning
G Farnadi, SH Bach, MF Moens, L Getoor, M De Cock
Machine Learning 106 (12), 1971-1991, 2017
292017
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
282019
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
252013
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
232012
Graph summarization in annotated data using probabilistic soft logic
A Memory, A Kimmig, S Bach, L Raschid, L Getoor
Proceedings of the 8th International Workshop on Uncertainty Reasoning for …, 2012
212012
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
202020
Unifying local consistency and MAX SAT relaxations for scalable inference with rounding guarantees
SH Bach, B Huang, L Getoor
Artificial Intelligence and Statistics (AISTATS), 2015
122015
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
112015
Learning latent groups with hinge-loss Markov random fields
S Bach, B Huang, L Getoor
102013
Decision-driven models with probabilistic soft logic
SH Bach, M Broecheler, S Kok, L Getoor
102010
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Articles 1–20