Stephen H. Bach
Stephen H. Bach
Assistant Professor of Computer Science, Brown University
Verified email at - Homepage
Cited by
Cited by
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
Bloom: A 176b-parameter open-access multilingual language model
T Le Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, R Castagné, ...
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
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
Paired learners for concept drift
SH Bach, M Maloof
IEEE International Conference on Data Mining (ICDM), 2008
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
Hinge-loss Markov random fields: Convex inference for structured prediction
SH Bach, B Huang, B London, L Getoor
Uncertainty in Artificial Intelligence (UAI), 2013
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
Weakly Supervised Sequence Tagging from Noisy Rules
E Safranchik, S Luo, SH Bach
AAAI Conference on Artificial Intelligence (AAAI), 2020
Low-resource languages jailbreak gpt-4
ZX Yong, C Menghini, SH Bach
arXiv preprint arXiv:2310.02446, 2023
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
Fairness via explanation quality: Evaluating disparities in the quality of post hoc explanations
J Dai, S Upadhyay, U Aivodji, SH Bach, H Lakkaraju
Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, 203-214, 2022
Learning to compose soft prompts for compositional zero-shot learning
NV Nayak, P Yu, SH Bach
arXiv preprint arXiv:2204.03574, 2022
A Bayesian approach to concept drift
S Bach, M Maloof
Advances in Neural Information Processing Systems (NIPS), 2010
Bigbio: A framework for data-centric biomedical natural language processing
J Fries, L Weber, N Seelam, G Altay, D Datta, S Garda, S Kang, R Su, ...
Advances in Neural Information Processing Systems 35, 25792-25806, 2022
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
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