Stephen H. Bach

According to our database, Stephen H. Bach authored at least 51 papers between 2008 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Planetarium: A Rigorous Benchmark for Translating Text to Structured Planning Languages.

Max Zuo, Francisco Piedrahita Velez, Xiaochen Li, Michael L. Littman, Stephen H. Bach

CoRR, 2024

Preference Tuning For Toxicity Mitigation Generalizes Across Languages.

Xiaochen Li, Zheng-Xin Yong, Stephen H. Bach

CoRR, 2024

Follow-Up Differential Descriptions: Language Models Resolve Ambiguities for Image Classification.

Reza Esfandiarpoor, Stephen H. Bach

Proceedings of the Twelfth International Conference on Learning Representations, 2024

LexC-Gen: Generating Data for Extremely Low-Resource Languages with Large Language Models and Bilingual Lexicons.

Zheng Xin Yong, Cristina Menghini, Stephen H. Bach

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

Preference Tuning For Toxicity Mitigation Generalizes Across Languages.

Xiaochen Li, Zheng Xin Yong, Stephen H. Bach

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

If CLIP Could Talk: Understanding Vision-Language Model Representations Through Their Preferred Concept Descriptions.

Reza Esfandiarpoor, Cristina Menghini, Stephen H. Bach

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Does CLIP Bind Concepts? Probing Compositionality in Large Image Models.

Martha Lewis, Nihal V. Nayak, Peilin Yu, Jack Merullo, Qinan Yu, Stephen H. Bach, Ellie Pavlick

Proceedings of the Findings of the Association for Computational Linguistics: EACL 2024, 2024

Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation.

Nihal V. Nayak, Yiyang Nan, Avi Trost, Stephen H. Bach

Proceedings of the Findings of the Association for Computational Linguistics, 2024

2023

Low-Resource Languages Jailbreak GPT-4.

Zheng Xin Yong, Cristina Menghini, Stephen H. Bach

CoRR, 2023

An Adaptive Method for Weak Supervision with Drifting Data.

Alessio Mazzetto, Reza Esfandiarpoor, Eli Upfal, Stephen H. Bach

CoRR, 2023

Enhancing CLIP with CLIP: Exploring Pseudolabeling for Limited-Label Prompt Tuning.

Cristina Menghini, Andrew Delworth, Stephen H. Bach

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning to Compose Soft Prompts for Compositional Zero-Shot Learning.

Nihal V. Nayak, Peilin Yu, Stephen H. Bach

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Leveraging Large Language Models for Structure Learning in Prompted Weak Supervision.

Jinyan Su, Peilin Yu, Jieyu Zhang, Stephen H. Bach

Proceedings of the IEEE International Conference on Big Data, 2023

Alfred: A System for Prompted Weak Supervision.

Peilin Yu, Stephen H. Bach

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 2023

2022

Zero-Shot Learning with Common Sense Knowledge Graphs.

Nihal V. Nayak, Stephen H. Bach

Trans. Mach. Learn. Res., 2022

BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing.

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

CoRR, 2022

Language Models in the Loop: Incorporating Prompting into Weak Supervision.

Ryan Smith, Jason A. Fries, Braden Hancock, Stephen H. Bach

CoRR, 2022

PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts.

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

CoRR, 2022

Tight Lower Bounds on Worst-Case Guarantees for Zero-Shot Learning with Attributes.

Alessio Mazzetto, Cristina Menghini, Andrew Yuan, Eli Upfal, Stephen H. Bach

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

BigBio: A Framework for Data-Centric Biomedical Natural Language Processing.

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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

TAGLETS: A System for Automatic Semi-Supervised Learning with Auxiliary Data.

Wasu Piriyakulkij, Cristina Menghini, Ross Briden, Nihal V. Nayak, Jeffrey Zhu, Elaheh Raisi, Stephen H. Bach

Proceedings of the Fifth Conference on Machine Learning and Systems, 2022

Multitask Prompted Training Enables Zero-Shot Task Generalization.

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

Proceedings of the Tenth International Conference on Learning Representations, 2022

Learning from Multiple Noisy Partial Labelers.

Peilin Yu, Tiffany Ding, Stephen H. Bach

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations.

Jessica Dai, Sohini Upadhyay, Ulrich Aïvodji, Stephen H. Bach, Himabindu Lakkaraju

Proceedings of the AIES '22: AAAI/ACM Conference on AI, Ethics, and Society, Oxford, United Kingdom, May 19, 2022

PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts.

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

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 2022

2021

Multitask Prompted Training Enables Zero-Shot Task Generalization.

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

CoRR, 2021

What will it take to generate fairness-preserving explanations?

Jessica Dai, Sohini Upadhyay, Stephen H. Bach, Himabindu Lakkaraju

CoRR, 2021

Adversarial Multi Class Learning under Weak Supervision with Performance Guarantees.

Alessio Mazzetto, Cyrus Cousins, Dylan Sam, Stephen H. Bach, Eli Upfal

Proceedings of the 38th International Conference on Machine Learning, 2021

Semi-Supervised Aggregation of Dependent Weak Supervision Sources With Performance Guarantees.

Alessio Mazzetto, Dylan Sam, Andrew Park, Eli Upfal, Stephen H. Bach

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Pseudo Shots: Few-Shot Learning with Auxiliary Data.

Reza Esfandiarpoor, Mohsen Hajabdollahi, Stephen H. Bach

CoRR, 2020

Selecting Auxiliary Data Using Knowledge Graphs for Image Classification with Limited Labels.

Elaheh Raisi, Stephen H. Bach

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Weakly Supervised Sequence Tagging from Noisy Rules.

Esteban Safranchik, Shiying Luo, Stephen H. Bach

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale.

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

Proceedings of the 2019 International Conference on Management of Data, 2019

2017

Snorkel: Rapid Training Data Creation with Weak Supervision.

Alexander Ratner, Stephen H. Bach, Henry R. Ehrenberg, Jason Alan Fries, Sen Wu, Christopher Ré

Proc. VLDB Endow., 2017

Soft quantification in statistical relational learning.

Golnoosh Farnadi, Stephen H. Bach, Marie-Francine Moens, Lise Getoor, Martine De Cock

Mach. Learn., 2017

Hinge-Loss Markov Random Fields and Probabilistic Soft Logic.

Stephen H. Bach, Matthias Broecheler, Bert Huang, Lise Getoor

J. Mach. Learn. Res., 2017

Snorkel: Fast Training Set Generation for Information Extraction.

Alexander J. Ratner, Stephen H. Bach, Henry R. Ehrenberg, Christopher Ré

Proceedings of the 2017 ACM International Conference on Management of Data, 2017

Learning the Structure of Generative Models without Labeled Data.

Stephen H. Bach, Bryan Dawei He, Alexander Ratner, Christopher Ré

Proceedings of the 34th International Conference on Machine Learning, 2017

Snorkel: A System for Lightweight Extraction.

Alexander Ratner, Stephen H. Bach, Henry R. Ehrenberg, Jason Alan Fries, Sen Wu, Christopher Ré

Proceedings of the 8th Biennial Conference on Innovative Data Systems Research, 2017

2016

Interpretable Decision Sets: A Joint Framework for Description and Prediction.

Himabindu Lakkaraju, Stephen H. Bach, Jure Leskovec

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

2015

Hinge-Loss Markov Random Fields and Probabilistic Soft Logic: A Scalable Approach to Structured Prediction.

Stephen H. Bach

PhD thesis, 2015

Statistical Relational Learning with Soft Quantifiers.

Golnoosh Farnadi, Stephen H. Bach, Marjon Blondeel, Marie-Francine Moens, Lise Getoor, Martine De Cock

Proceedings of the Inductive Logic Programming - 25th International Conference, 2015

Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs.

Stephen H. Bach, Bert Huang, Jordan L. Boyd-Graber, Lise Getoor

Proceedings of the 32nd International Conference on Machine Learning, 2015

Unifying Local Consistency and MAX SAT Relaxations for Scalable Inference with Rounding Guarantees.

Stephen H. Bach, Bert Huang, Lise Getoor

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Extending PSL with Fuzzy Quantifiers.

Golnoosh Farnadi, Stephen H. Bach, Marie-Francine Moens, Lise Getoor, Martine De Cock

Proceedings of the Statistical Relational Artificial Intelligence, 2014

2013

Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction.

Stephen H. Bach, Bert Huang, Ben London, Lise Getoor

Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

Collective Activity Detection Using Hinge-loss Markov Random Fields.

Ben London, Sameh Khamis, Stephen H. Bach, Bert Huang, Lise Getoor, Larry S. Davis

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013

2012

Graph Summarization in Annotated Data Using Probabilistic Soft Logic.

Alex Memory, Angelika Kimmig, Stephen H. Bach, Louiqa Raschid, Lise Getoor

Proceedings of the 8th International Workshop on Uncertainty Reasoning for the Semantic Web, 2012

Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization.

Stephen H. Bach, Matthias Broecheler, Lise Getoor, Dianne P. O'Leary

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2010

A Bayesian Approach to Concept Drift.

Stephen H. Bach, Marcus A. Maloof

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

2008

Paired Learners for Concept Drift.

Stephen H. Bach, Marcus A. Maloof

Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), 2008