Stephen H. Bach

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Lise GetoorDistinguished Professor of Computer Science & Engineering, University of California, Santa CruzDirección de correo verificada de ucsc.edu
Bert HuangAssistant Professor, Tufts University Department of Computer ScienceDirección de correo verificada de vt.edu
Alex RatnerAssistant Professor, University of WashingtonDirección de correo verificada de cs.washington.edu
Christopher RéComputer Science, Stanford UniversityDirección de correo verificada de cs.stanford.edu
Henry R. EhrenbergFacebook, Stanford UniversityDirección de correo verificada de stanford.edu
Himabindu LakkarajuAssistant Professor, Harvard UniversityDirección de correo verificada de seas.harvard.edu
Jure LeskovecProfessor of Computer Science, Stanford UniversityDirección de correo verificada de cs.stanford.edu
Angelika KimmigKU LeuvenDirección de correo verificada de cs.kuleuven.be
Bryan HeStanford UniversityDirección de correo verificada de stanford.edu
Dianne P O'LearyProfessor of Computer Science, University of MarylandDirección de correo verificada de cs.umd.edu
Sameh KhamisCo-Founder and Director of R&D, RembrandDirección de correo verificada de rembrand.com
Larry DavisProfessor of Computer Science, University of MarylandDirección de correo verificada de cs.umd.edu
Louiqa RaschidProfessor, University of MarylandDirección de correo verificada de umiacs.umd.edu
Stanley KokNational University of SingaporeDirección de correo verificada de nus.edu.sg

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Stephen H. Bach

Assistant Professor of Computer Science, Brown University

Dirección de correo verificada de cs.brown.edu - Página principal

Machine Learning


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Multitask prompted training enables zero-shot task generalization V Sanh, A Webson, C Raffel, S Bach, L Sutawika, Z Alyafeai, A Chaffin, ... International Conference on Learning Representations (ICLR), 2022	1702	2022
Bloom: A 176b-parameter open-access multilingual language model T Le Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, R Castagné, ...	1626	2022
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	1346	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	993	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	522	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	327	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	319	2022
Paired learners for concept drift SH Bach, M Maloof IEEE International Conference on Data Mining (ICDM), 2008	222	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	194	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	168	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	147	2013
Low-resource languages jailbreak gpt-4 ZX Yong, C Menghini, SH Bach arXiv preprint arXiv:2310.02446, 2023	140	2023
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	102	2017
Weakly Supervised Sequence Tagging from Noisy Rules E Safranchik, S Luo, SH Bach AAAI Conference on Artificial Intelligence (AAAI), 2020	99	2020
Learning to compose soft prompts for compositional zero-shot learning NV Nayak, P Yu, SH Bach arXiv preprint arXiv:2204.03574, 2022	68	2022
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	66	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	63	2022
Language models in the loop: Incorporating prompting into weak supervision R Smith, JA Fries, B Hancock, SH Bach ACM/JMS Journal of Data Science 1 (2), 1-30, 2024	48	2024
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	48	2022
Does CLIP bind concepts? Probing compositionality in large image models M Lewis, NV Nayak, P Yu, Q Yu, J Merullo, SH Bach, E Pavlick arXiv preprint arXiv:2212.10537, 2022	44	2022

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