Stephen H. Bach

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 53

Top Venues

CoRR

AISTATS

ICML

NeurIPS

University of Maryland, College Park, MD, USA (PHD)

Education History

University of Maryland, College Park, MD, USA

PHD

Publications

Reza Esfandiarpoor, Stephen H. Bach
Follow-Up Differential Descriptions: Language Models Resolve Ambiguities for Image Classification. ICLR (2024)
Nihal V. Nayak, Yiyang Nan, Avi Trost, Stephen H. Bach
Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation. CoRR (2024)
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)
Xiaochen Li, Zheng-Xin Yong, Stephen H. Bach
Preference Tuning For Toxicity Mitigation Generalizes Across Languages. CoRR (2024)
Nihal V. Nayak, Yiyang Nan, Avi Trost, Stephen H. Bach
Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation. ACL (Findings) (2024)
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 (2024)
Jinyan Su, Peilin Yu, Jieyu Zhang, Stephen H. Bach
Leveraging Large Language Models for Structure Learning in Prompted Weak Supervision. CoRR (2024)
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 (2024)
Reza Esfandiarpoor, Cristina Menghini, Stephen H. Bach
If CLIP Could Talk: Understanding Vision-Language Model Representations Through Their Preferred Concept Descriptions. CoRR (2024)
Reza Esfandiarpoor, Stephen H. Bach
Follow-Up Differential Descriptions: Language Models Resolve Ambiguities for Image Classification. CoRR (2023)
Alessio Mazzetto, Reza Esfandiarpoor, Eli Upfal, Stephen H. Bach
An Adaptive Method for Weak Supervision with Drifting Data. CoRR (2023)
Jinyan Su, Peilin Yu, Jieyu Zhang, Stephen H. Bach
Leveraging Large Language Models for Structure Learning in Prompted Weak Supervision. BigData (2023)
Nihal V. Nayak, Peilin Yu, Stephen H. Bach
Learning to Compose Soft Prompts for Compositional Zero-Shot Learning. ICLR (2023)
Cristina Menghini, Andrew Delworth, Stephen H. Bach
Enhancing CLIP with CLIP: Exploring Pseudolabeling for Limited-Label Prompt Tuning. CoRR (2023)
Peilin Yu, Stephen H. Bach
Alfred: A System for Prompted Weak Supervision. CoRR (2023)
Peilin Yu, Stephen H. Bach
Alfred: A System for Prompted Weak Supervision. ACL (demo) (2023)
Zheng Xin Yong, Cristina Menghini, Stephen H. Bach
Low-Resource Languages Jailbreak GPT-4. CoRR (2023)
Cristina Menghini, Andrew Delworth, Stephen H. Bach
Enhancing CLIP with CLIP: Exploring Pseudolabeling for Limited-Label Prompt Tuning. NeurIPS (2023)
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 (2022)
Nihal V. Nayak, Stephen H. Bach
Zero-Shot Learning with Common Sense Knowledge Graphs. Trans. Mach. Learn. Res. 2022 (2022)
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 (2022)
Nihal V. Nayak, Peilin Yu, Stephen H. Bach
Learning to Compose Soft Prompts for Compositional Zero-Shot Learning. CoRR (2022)
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 (2022)
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)
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)
Peilin Yu, Tiffany Ding, Stephen H. Bach
Learning from Multiple Noisy Partial Labelers. AISTATS (2022)
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)
Ryan Smith, Jason A. Fries, Braden Hancock, Stephen H. Bach
Language Models in the Loop: Incorporating Prompting into Weak Supervision. CoRR (2022)
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)
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 (2022)
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)
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 (2021)
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 (2021)
Peilin Yu, Tiffany Ding, Stephen H. Bach
Learning from Multiple Noisy Partial Labelers. CoRR (2021)
Alessio Mazzetto, Dylan Sam, Andrew Park, Eli Upfal, Stephen H. Bach
Semi-Supervised Aggregation of Dependent Weak Supervision Sources With Performance Guarantees. AISTATS (2021)
Jessica Dai, Sohini Upadhyay, Stephen H. Bach, Himabindu Lakkaraju
What will it take to generate fairness-preserving explanations? CoRR (2021)
Alessio Mazzetto, Cyrus Cousins, Dylan Sam, Stephen H. Bach, Eli Upfal
Adversarial Multi Class Learning under Weak Supervision with Performance Guarantees. ICML (2021)
Elaheh Raisi, Stephen H. Bach
Selecting Auxiliary Data Using Knowledge Graphs for Image Classification with Limited Labels. CVPR Workshops (2020)
Esteban Safranchik, Shiying Luo, Stephen H. Bach
Weakly Supervised Sequence Tagging from Noisy Rules. AAAI (2020)
Nihal V. Nayak, Stephen H. Bach
Zero-Shot Learning with Common Sense Knowledge Graphs. CoRR (2020)
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) (2020)
Reza Esfandiarpoor, Mohsen Hajabdollahi, Stephen H. Bach
Pseudo Shots: Few-Shot Learning with Auxiliary Data. CoRR (2020)
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)
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 (2018)
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) (2017)
Golnoosh Farnadi, Stephen H. Bach, Marie-Francine Moens, Lise Getoor, Martine De Cock
Soft quantification in statistical relational learning. Mach. Learn. 106 (12) (2017)
Alexander Ratner, Stephen H. Bach, Henry R. Ehrenberg, Jason Alan Fries, Sen Wu, Christopher Ré
Snorkel: A System for Lightweight Extraction. CIDR (2017)
Stephen H. Bach, Matthias Broecheler, Bert Huang, Lise Getoor
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic. J. Mach. Learn. Res. 18 (2017)
Alexander J. Ratner, Stephen H. Bach, Henry R. Ehrenberg, Christopher Ré
Snorkel: Fast Training Set Generation for Information Extraction. SIGMOD Conference (2017)
Alexander Ratner, Stephen H. Bach, Henry R. Ehrenberg, Jason Alan Fries, Sen Wu, Christopher Ré
Snorkel: Rapid Training Data Creation with Weak Supervision. CoRR (2017)
Stephen H. Bach, Bryan Dawei He, Alexander Ratner, Christopher Ré
Learning the Structure of Generative Models without Labeled Data. CoRR (2017)
Stephen H. Bach, Bryan Dawei He, Alexander Ratner, Christopher Ré
Learning the Structure of Generative Models without Labeled Data. ICML (2017)
Himabindu Lakkaraju, Stephen H. Bach, Jure Leskovec
Interpretable Decision Sets: A Joint Framework for Description and Prediction. KDD (2016)
Stephen H. Bach, Bert Huang, Lise Getoor
Unifying Local Consistency and MAX SAT Relaxations for Scalable Inference with Rounding Guarantees. AISTATS (2015)
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)
Golnoosh Farnadi, Stephen H. Bach, Marjon Blondeel, Marie-Francine Moens, Lise Getoor, Martine De Cock
Statistical Relational Learning with Soft Quantifiers. ILP (2015)
Stephen H. Bach, Matthias Broecheler, Bert Huang, Lise Getoor
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic. CoRR (2015)
Golnoosh Farnadi, Stephen H. Bach, Marie-Francine Moens, Lise Getoor, Martine De Cock
Extending PSL with Fuzzy Quantifiers. AAAI Workshop: Statistical Relational Artificial Intelligence (2014)
Stephen H. Bach, Bert Huang, Ben London, Lise Getoor
Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction. CoRR (2013)
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)
Stephen H. Bach, Bert Huang, Ben London, Lise Getoor
Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction. UAI (2013)
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)
Alex Memory, Angelika Kimmig, Stephen H. Bach, Louiqa Raschid, Lise Getoor
Graph Summarization in Annotated Data Using Probabilistic Soft Logic. URSW (2012)
Stephen H. Bach, Marcus A. Maloof
A Bayesian Approach to Concept Drift. NIPS (2010)
Stephen H. Bach, Marcus A. Maloof
Paired Learners for Concept Drift. ICDM (2008)

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Publication Activity (10 Years)

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Stephen H. Bach