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ORCIDStephen H. Bach
Person information
- affiliation: Brown University, Providence, RI, USA
- affiliation (former): Stanford University
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Books and Theses
- 2015
[b1]Stephen H. Bach:
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic: A Scalable Approach to Structured Prediction. University of Maryland, College Park, MD, USA, 2015
Journal Articles
- 2022
- [j5]Nihal V. Nayak, Stephen H. Bach:
Zero-Shot Learning with Common Sense Knowledge Graphs. Trans. Mach. Learn. Res. 2022 (2022) - 2020
- [j4]Alexander Ratner, Stephen H. Bach, Henry R. Ehrenberg, Jason A. Fries
, Sen Wu, Christopher Ré:
Snorkel: rapid training data creation with weak supervision. VLDB J. 29(2-3): 709-730 (2020) - 2017
- [j3]Stephen H. Bach, Matthias Broecheler, Bert Huang, Lise Getoor:
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic. J. Mach. Learn. Res. 18: 109:1-109:67 (2017) - [j2]Golnoosh Farnadi, Stephen H. Bach, Marie-Francine Moens, Lise Getoor, Martine De Cock:
Soft quantification in statistical relational learning. Mach. Learn. 106(12): 1971-1991 (2017) - [j1]Alexander Ratner, Stephen H. Bach, Henry R. Ehrenberg, Jason Alan Fries, Sen Wu, Christopher Ré:
Snorkel: Rapid Training Data Creation with Weak Supervision. Proc. VLDB Endow. 11(3): 269-282 (2017)
Conference and Workshop Papers
- 2024
- [c33]Nihal V. Nayak, Yiyang Nan, Avi Trost, Stephen H. Bach:
Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation. ACL (Findings) 2024: 12585-12611 - [c32]Martha Lewis, Nihal V. Nayak, Peilin Yu, Jack Merullo, Qinan Yu, Stephen H. Bach, Ellie Pavlick:
Does CLIP Bind Concepts? Probing Compositionality in Large Image Models. EACL (Findings) 2024: 1487-1500 - [c31]Reza Esfandiarpoor, Stephen H. Bach:
Follow-Up Differential Descriptions: Language Models Resolve Ambiguities for Image Classification. ICLR 2024 - 2023
- [c30]Peilin Yu, Stephen H. Bach:
Alfred: A System for Prompted Weak Supervision. ACL (demo) 2023: 479-488 - [c29]Jinyan Su, Peilin Yu, Jieyu Zhang, Stephen H. Bach:
Leveraging Large Language Models for Structure Learning in Prompted Weak Supervision. IEEE Big Data 2023: 875-884 - [c28]Nihal V. Nayak, Peilin Yu, Stephen H. Bach:
Learning to Compose Soft Prompts for Compositional Zero-Shot Learning. ICLR 2023 - [c27]Cristina Menghini, Andrew Delworth, Stephen H. Bach:
Enhancing CLIP with CLIP: Exploring Pseudolabeling for Limited-Label Prompt Tuning. NeurIPS 2023 - 2022
- [c26]Stephen H. Bach, Victor Sanh, Zheng Xin Yong, Albert Webson, Colin Raffel, Nihal V. Nayak, Abheesht Sharma, Taewoon Kim, M. Saiful Bari, Thibault Févry, Zaid Alyafeai, Manan Dey, Andrea Santilli, Zhiqing Sun, Srulik Ben-David, Canwen Xu, Gunjan Chhablani, Han Wang, Jason Alan Fries, Maged Saeed AlShaibani, Shanya Sharma, Urmish Thakker, Khalid Almubarak, Xiangru Tang, Dragomir R. Radev, Mike Tian-Jian Jiang, Alexander M. Rush:
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts. ACL (demo) 2022: 93-104 - [c25]Jessica Dai, Sohini Upadhyay, Ulrich Aïvodji, Stephen H. Bach, Himabindu Lakkaraju:
Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations. AIES 2022: 203-214 - [c24]Peilin Yu, Tiffany Ding, Stephen H. Bach:
Learning from Multiple Noisy Partial Labelers. AISTATS 2022: 11072-11095 - [c23]Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal V. Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Févry, Jason Alan Fries, Ryan Teehan, Teven Le Scao, Stella Biderman, Leo Gao, Thomas Wolf, Alexander M. Rush:
Multitask Prompted Training Enables Zero-Shot Task Generalization. ICLR 2022 - [c22]Wasu Piriyakulkij, Cristina Menghini, Ross Briden, Nihal V. Nayak, Jeffrey Zhu, Elaheh Raisi, Stephen H. Bach:
TAGLETS: A System for Automatic Semi-Supervised Learning with Auxiliary Data. MLSys 2022 - [c21]Jason A. Fries, Leon Weber, Natasha Seelam, Gabriel Altay, Debajyoti Datta, Samuele Garda, Sunny Kang, Rosaline Su, Wojciech Kusa, Samuel Cahyawijaya, Fabio Barth, Simon Ott, Matthias Samwald, Stephen H. Bach, Stella Biderman, Mario Sänger, Bo Wang, Alison Callahan, Daniel León Periñán, Théo Gigant, Patrick Haller, Jenny Chim, José D. Posada, John M. Giorgi, Karthik Rangasai Sivaraman, Marc Pàmies, Marianna Nezhurina, Robert Martin, Michael Cullan, Moritz Freidank, Nathan Dahlberg, Shubhanshu Mishra, Shamik Bose, Nicholas Broad, Yanis Labrak, Shlok Deshmukh, Sid Kiblawi, Ayush Singh, Minh Chien Vu, Trishala Neeraj, Jonas Golde, Albert Villanova del Moral, Benjamin Beilharz:
BigBio: A Framework for Data-Centric Biomedical Natural Language Processing. NeurIPS 2022 - [c20]Alessio Mazzetto, Cristina Menghini, Andrew Yuan, Eli Upfal, Stephen H. Bach:
Tight Lower Bounds on Worst-Case Guarantees for Zero-Shot Learning with Attributes. NeurIPS 2022 - 2021
- [c19]Alessio Mazzetto, Dylan Sam, Andrew Park, Eli Upfal, Stephen H. Bach:
Semi-Supervised Aggregation of Dependent Weak Supervision Sources With Performance Guarantees. AISTATS 2021: 3196-3204 - [c18]Alessio Mazzetto, Cyrus Cousins, Dylan Sam, Stephen H. Bach, Eli Upfal:
Adversarial Multi Class Learning under Weak Supervision with Performance Guarantees. ICML 2021: 7534-7543 - 2020
- [c17]Esteban Safranchik, Shiying Luo, Stephen H. Bach:
Weakly Supervised Sequence Tagging from Noisy Rules. AAAI 2020: 5570-5578 - [c16]Elaheh Raisi, Stephen H. Bach:
Selecting Auxiliary Data Using Knowledge Graphs for Image Classification with Limited Labels. CVPR Workshops 2020: 4026-4031 - 2019
- [c15]Stephen H. Bach, Daniel Rodriguez, Yintao Liu, Chong Luo, Haidong Shao, Cassandra Xia, Souvik Sen, Alexander Ratner, Braden Hancock, Houman Alborzi, Rahul Kuchhal, Christopher Ré, Rob Malkin:
Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale. SIGMOD Conference 2019: 362-375 - 2017
- [c14]Alexander Ratner, Stephen H. Bach, Henry R. Ehrenberg, Jason Alan Fries, Sen Wu, Christopher Ré:
Snorkel: A System for Lightweight Extraction. CIDR 2017 - [c13]Stephen H. Bach, Bryan Dawei He, Alexander Ratner, Christopher Ré:
Learning the Structure of Generative Models without Labeled Data. ICML 2017: 273-282 - [c12]Alexander J. Ratner, Stephen H. Bach, Henry R. Ehrenberg, Christopher Ré:
Snorkel: Fast Training Set Generation for Information Extraction. SIGMOD Conference 2017: 1683-1686 - 2016
- [c11]Himabindu Lakkaraju, Stephen H. Bach, Jure Leskovec:
Interpretable Decision Sets: A Joint Framework for Description and Prediction. KDD 2016: 1675-1684 - 2015
- [c10]Stephen H. Bach, Bert Huang, Lise Getoor:
Unifying Local Consistency and MAX SAT Relaxations for Scalable Inference with Rounding Guarantees. AISTATS 2015 - [c9]Stephen H. Bach, Bert Huang, Jordan L. Boyd-Graber, Lise Getoor:
Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs. ICML 2015: 381-390 - [c8]Golnoosh Farnadi, Stephen H. Bach, Marjon Blondeel, Marie-Francine Moens, Lise Getoor, Martine De Cock:
Statistical Relational Learning with Soft Quantifiers. ILP 2015: 60-75 - 2014
- [c7]Golnoosh Farnadi, Stephen H. Bach, Marie-Francine Moens, Lise Getoor, Martine De Cock:
Extending PSL with Fuzzy Quantifiers. StarAI@AAAI 2014 - 2013
- [c6]Ben London, Sameh Khamis, Stephen H. Bach, Bert Huang, Lise Getoor, Larry S. Davis:
Collective Activity Detection Using Hinge-loss Markov Random Fields. CVPR Workshops 2013: 566-571 - [c5]Stephen H. Bach, Bert Huang, Ben London, Lise Getoor:
Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction. UAI 2013 - 2012
- [c4]Stephen H. Bach, Matthias Broecheler, Lise Getoor, Dianne P. O'Leary:
Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization. NIPS 2012: 2663-2671 - [c3]Alex Memory, Angelika Kimmig, Stephen H. Bach, Louiqa Raschid, Lise Getoor:
Graph Summarization in Annotated Data Using Probabilistic Soft Logic. URSW 2012: 75-86 - 2010
- [c2]Stephen H. Bach, Marcus A. Maloof:
A Bayesian Approach to Concept Drift. NIPS 2010: 127-135 - 2008
- [c1]Stephen H. Bach, Marcus A. Maloof:
Paired Learners for Concept Drift. ICDM 2008: 23-32
Informal and Other Publications
- 2024
- [i28]Jinyan Su, Peilin Yu, Jieyu Zhang, Stephen H. Bach:
Leveraging Large Language Models for Structure Learning in Prompted Weak Supervision. CoRR abs/2402.01867 (2024) - [i27]Zheng Xin Yong, Cristina Menghini, Stephen H. Bach:
LexC-Gen: Generating Data for Extremely Low-Resource Languages with Large Language Models and Bilingual Lexicons. CoRR abs/2402.14086 (2024) - [i26]Nihal V. Nayak, Yiyang Nan, Avi Trost, Stephen H. Bach:
Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation. CoRR abs/2402.18334 (2024) - [i25]Reza Esfandiarpoor, Cristina Menghini, Stephen H. Bach:
If CLIP Could Talk: Understanding Vision-Language Model Representations Through Their Preferred Concept Descriptions. CoRR abs/2403.16442 (2024) - [i24]Xiaochen Li, Zheng-Xin Yong, Stephen H. Bach:
Preference Tuning For Toxicity Mitigation Generalizes Across Languages. CoRR abs/2406.16235 (2024) - [i23]Max Zuo, Francisco Piedrahita Velez, Xiaochen Li, Michael L. Littman, Stephen H. Bach:
Planetarium: A Rigorous Benchmark for Translating Text to Structured Planning Languages. CoRR abs/2407.03321 (2024) - 2023
- [i22]Peilin Yu, Stephen H. Bach:
Alfred: A System for Prompted Weak Supervision. CoRR abs/2305.18623 (2023) - [i21]Alessio Mazzetto, Reza Esfandiarpoor, Eli Upfal, Stephen H. Bach:
An Adaptive Method for Weak Supervision with Drifting Data. CoRR abs/2306.01658 (2023) - [i20]Cristina Menghini, Andrew Delworth, Stephen H. Bach:
Enhancing CLIP with CLIP: Exploring Pseudolabeling for Limited-Label Prompt Tuning. CoRR abs/2306.01669 (2023) - [i19]Zheng Xin Yong, Cristina Menghini, Stephen H. Bach:
Low-Resource Languages Jailbreak GPT-4. CoRR abs/2310.02446 (2023) - [i18]Reza Esfandiarpoor, Stephen H. Bach:
Follow-Up Differential Descriptions: Language Models Resolve Ambiguities for Image Classification. CoRR abs/2311.07593 (2023) - 2022
- [i17]Stephen H. Bach, Victor Sanh, Zheng Xin Yong, Albert Webson, Colin Raffel, Nihal V. Nayak, Abheesht Sharma, Taewoon Kim, M. Saiful Bari, Thibault Févry, Zaid Alyafeai, Manan Dey, Andrea Santilli, Zhiqing Sun, Srulik Ben-David, Canwen Xu, Gunjan Chhablani, Han Wang, Jason Alan Fries, Maged Saeed AlShaibani, Shanya Sharma, Urmish Thakker, Khalid Almubarak, Xiangru Tang, Mike Tian-Jian Jiang, Alexander M. Rush:
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts. CoRR abs/2202.01279 (2022) - [i16]Nihal V. Nayak, Peilin Yu, Stephen H. Bach:
Learning to Compose Soft Prompts for Compositional Zero-Shot Learning. CoRR abs/2204.03574 (2022) - [i15]Ryan Smith, Jason A. Fries, Braden Hancock, Stephen H. Bach:
Language Models in the Loop: Incorporating Prompting into Weak Supervision. CoRR abs/2205.02318 (2022) - [i14]Jessica Dai, Sohini Upadhyay, Ulrich Aïvodji, Stephen H. Bach, Himabindu Lakkaraju:
Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations. CoRR abs/2205.07277 (2022) - [i13]Alessio Mazzetto, Cristina Menghini, Andrew Yuan, Eli Upfal, Stephen H. Bach:
Tight Lower Bounds on Worst-Case Guarantees for Zero-Shot Learning with Attributes. CoRR abs/2205.13068 (2022) - [i12]Jason Alan Fries, Leon Weber, Natasha Seelam, Gabriel Altay, Debajyoti Datta, Samuele Garda, Myungsun Kang, Ruisi Su, Wojciech Kusa, Samuel Cahyawijaya, Fabio Barth, Simon Ott, Matthias Samwald, Stephen H. Bach, Stella Biderman, Mario Sänger, Bo Wang, Alison Callahan, Daniel León Periñán, Théo Gigant, Patrick Haller, Jenny Chim, José David Posada, John Michael Giorgi, Karthik Rangasai Sivaraman, Marc Pàmies, Marianna Nezhurina, Robert Martin, Michael Cullan, Moritz Freidank, Nathan Dahlberg, Shubhanshu Mishra, Shamik Bose, Nicholas Michio Broad, Yanis Labrak, Shlok S. Deshmukh, Sid Kiblawi, Ayush Singh, Minh Chien Vu, Trishala Neeraj, Jonas Golde, Albert Villanova del Moral, Benjamin Beilharz:
BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing. CoRR abs/2206.15076 (2022) - 2021
- [i11]Peilin Yu, Tiffany Ding, Stephen H. Bach:
Learning from Multiple Noisy Partial Labelers. CoRR abs/2106.04530 (2021) - [i10]Jessica Dai, Sohini Upadhyay, Stephen H. Bach, Himabindu Lakkaraju:
What will it take to generate fairness-preserving explanations? CoRR abs/2106.13346 (2021) - [i9]Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M. Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal V. Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Févry, Jason Alan Fries, Ryan Teehan, Stella Biderman, Leo Gao, Tali Bers, Thomas Wolf, Alexander M. Rush:
Multitask Prompted Training Enables Zero-Shot Task Generalization. CoRR abs/2110.08207 (2021) - [i8]Wasu Piriyakulkij, Cristina Menghini, Ross Briden, Nihal V. Nayak, Jeffrey Zhu, Elaheh Raisi, Stephen H. Bach:
TAGLETS: A System for Automatic Semi-Supervised Learning with Auxiliary Data. CoRR abs/2111.04798 (2021) - 2020
- [i7]Nihal V. Nayak, Stephen H. Bach:
Zero-Shot Learning with Common Sense Knowledge Graphs. CoRR abs/2006.10713 (2020) - [i6]Reza Esfandiarpoor, Mohsen Hajabdollahi, Stephen H. Bach:
Pseudo Shots: Few-Shot Learning with Auxiliary Data. CoRR abs/2012.07176 (2020) - 2018
- [i5]Stephen H. Bach, Daniel Rodriguez, Yintao Liu, Chong Luo, Haidong Shao, Cassandra Xia, Souvik Sen, Alexander Ratner, Braden Hancock, Houman Alborzi, Rahul Kuchhal, Christopher Ré, Rob Malkin:
Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale. CoRR abs/1812.00417 (2018) - 2017
- [i4]Stephen H. Bach, Bryan Dawei He, Alexander Ratner, Christopher Ré:
Learning the Structure of Generative Models without Labeled Data. CoRR abs/1703.00854 (2017) - [i3]Alexander Ratner, Stephen H. Bach, Henry R. Ehrenberg, Jason Alan Fries, Sen Wu, Christopher Ré:
Snorkel: Rapid Training Data Creation with Weak Supervision. CoRR abs/1711.10160 (2017) - 2015
- [i2]Stephen H. Bach, Matthias Broecheler, Bert Huang, Lise Getoor:
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic. CoRR abs/1505.04406 (2015) - 2013
- [i1]Stephen H. Bach, Bert Huang, Ben London, Lise Getoor:
Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction. CoRR abs/1309.6813 (2013)
Coauthor Index
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