Data Programming: Creating Large Training Sets, Quickly. - Alexander J. Ratner; Christopher De Sa; Sen Wu 002; Daniel Selsam; Christopher Ré (NIPS, 2016)

Data programming with DDLite: putting humans in a different part of the loop. - Henry R. Ehrenberg; Jaeho Shin 001; Alexander J. Ratner; Jason A. Fries; Christopher Ré (HILDA@SIGMOD, 2016)

DeepDive: Declarative Knowledge Base Construction. - Christopher De Sa; Alexander Ratner; Christopher Ré; Jaeho Shin 001; Feiran Wang; Sen Wu 002; Ce Zhang 001 (SIGMOD Rec., 2016)

Snorkel: A System for Lightweight Extraction. - Alexander Ratner; Stephen H. Bach; Henry R. Ehrenberg; Jason Alan Fries; Sen Wu 002; Christopher Ré (CIDR, 2017)

Learning the Structure of Generative Models without Labeled Data. - Stephen H. Bach; Bryan Dawei He; Alexander Ratner; Christopher Ré (ICML, 2017)

Learning to Compose Domain-Specific Transformations for Data Augmentation. - Alexander J. Ratner; Henry R. Ehrenberg; Zeshan Hussain; Jared Dunnmon; Christopher Ré (NIPS, 2017)

Snorkel: Fast Training Set Generation for Information Extraction. - Alexander J. Ratner; Stephen H. Bach; Henry R. Ehrenberg; Christopher Ré (SIGMOD Conference, 2017)

SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data. - Jason A. Fries; Sen Wu 002; Alexander Ratner; Christopher Ré (CoRR, 2017)

Snorkel: Rapid Training Data Creation with Weak Supervision. - Alexander Ratner; Stephen H. Bach; Henry R. Ehrenberg; Jason Alan Fries; Sen Wu 002; Christopher Ré (Proc. VLDB Endow., 2017)

Incremental knowledge base construction using DeepDive. - Christopher De Sa; Alexander Ratner; Christopher Ré; Jaeho Shin 001; Feiran Wang; Sen Wu 002; Ce Zhang 001 (VLDB J., 2017)

Snorkel MeTaL: Weak Supervision for Multi-Task Learning. - Alexander Ratner; Braden Hancock; Jared Dunnmon; Roger E. Goldman; Christopher Ré (DEEM@SIGMOD, 2018)

Research for practice: knowledge base construction in the machine-learning era. - Alexander Ratner; Christopher Ré; Peter Bailis (Commun. ACM, 2018)

Knowledge Base Construction in the Machine-learning Era. - Alexander Ratner; Christopher Ré (ACM Queue, 2018)

Training Complex Models with Multi-Task Weak Supervision. - Alexander Ratner; Braden Hancock; Jared Dunnmon; Frederic Sala; Shreyash Pandey; Christopher Ré (AAAI, 2019)

The Role of Massively Multi-Task and Weak Supervision in Software 2.0. - Alexander J. Ratner; Braden Hancock; Christopher Ré (CIDR, 2019)

A Kernel Theory of Modern Data Augmentation. - Tri Dao; Albert Gu; Alexander Ratner; Virginia Smith; Chris De Sa; Christopher Ré (ICML, 2019)

Learning Dependency Structures for Weak Supervision Models. - Paroma Varma; Frederic Sala; Ann He; Alexander Ratner; Christopher Ré (ICML, 2019)

Doubly Weak Supervision of Deep Learning Models for Head CT. - Khaled Saab 002; Jared Dunnmon; Roger E. Goldman; Alexander Ratner; Hersh Sagreiya; Christopher Ré; Daniel L. Rubin (MICCAI (3), 2019)

Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices. - Vincent S. Chen; Sen Wu 002; Alexander J. Ratner; Jen Weng; Christopher Ré (NeurIPS, 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; et al. (SIGMOD Conference, 2019)

Osprey: Weak Supervision of Imbalanced Extraction Problems without Code. - Eran Bringer; Abraham Israeli; Yoav Shoham; Alexander Ratner; Christopher Ré (DEEM@SIGMOD, 2019)

SysML: The New Frontier of Machine Learning Systems. - Alexander Ratner; Dan Alistarh; Gustavo Alonso; David G. Andersen; Peter Bailis; Sarah Bird; Nicholas Carlini; Bryan Catanzaro; Eric S. Chung; Bill Dally; Jeff Dean; Inderjit S. Dhillon; et al. (CoRR, 2019)

Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices. - Vincent S. Chen; Sen Wu 002; Zhenzhen Weng; Alexander Ratner; Christopher Ré (CoRR, 2019)

Accelerating machine learning with training data management. - Alexander Ratner (2019)

Extracting chemical reactions from text using Snorkel. - Emily K. Mallory; Matthieu de Rochemonteix; Alexander Ratner; Ambika Acharya; Christopher Ré; Roselie A. Bright; Russ B. Altman (BMC Bioinform., 2020)

Cross-Modal Data Programming Enables Rapid Medical Machine Learning. - Jared A. Dunnmon; Alexander J. Ratner; Khaled Saab 002; Nishith Khandwala; Matthew Markert; Hersh Sagreiya; Roger E. Goldman; Christopher Lee-Messer; Matthew P. Lungren; Daniel L. Rubin; Christopher Ré (Patterns, 2020)

WRENCH: A Comprehensive Benchmark for Weak Supervision. - Jieyu Zhang 001; Yue Yu 001; Yinghao Li; Yujing Wang; Yaming Yang 001; Mao Yang 004; Alexander Ratner (NeurIPS Datasets and Benchmarks, 2021)

Proceedings of the First Workshop on Weakly Supervised Learning (WeaSuL). - Michael A. Hedderich; Benjamin Roth 001; Katharina Kann; Barbara Plank; Alex Ratner; Dietrich Klakow (CoRR, 2021)

Binary Classification with Positive Labeling Sources. - Jieyu Zhang 001; Yujing Wang; Yaming Yang 001; Yang Luo; Alexander Ratner (CIKM, 2022)

Creating Training Sets via Weak Indirect Supervision. - Jieyu Zhang 001; Bohan Wang; Xiangchen Song; Yujing Wang; Yaming Yang 001; Jing Bai 010; Alexander Ratner (ICLR, 2022)

Understanding Programmatic Weak Supervision via Source-aware Influence Function. - Jieyu Zhang 001; Haonan Wang; Cheng-Yu Hsieh; Alexander J. Ratner (NeurIPS, 2022)

A Survey on Programmatic Weak Supervision. - Jieyu Zhang 001; Cheng-Yu Hsieh; Yue Yu 001; Chao Zhang 014; Alexander Ratner (CoRR, 2022)

Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming. - Cheng-Yu Hsieh; Jieyu Zhang 001; Alexander J. Ratner (Proc. VLDB Endow., 2022)

Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes. - Cheng-Yu Hsieh; Chun-Liang Li; Chih-Kuan Yeh; Hootan Nakhost; Yasuhisa Fujii; Alex Ratner; Ranjay Krishna; Chen-Yu Lee; Tomas Pfister (ACL (Findings), 2023)

Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision. - Jieyu Zhang 001; Linxin Song; Alex Ratner (AISTATS, 2023)

DataComp: In search of the next generation of multimodal datasets. - Samir Yitzhak Gadre; Gabriel Ilharco; Alex Fang; Jonathan Hayase; Georgios Smyrnis; Thao Nguyen; Ryan Marten; Mitchell Wortsman; Dhruba Ghosh; Jieyu Zhang 001; Eyal Orgad; Rahim Entezari; et al. (NeurIPS, 2023)

Characterizing the Impacts of Semi-supervised Learning for Weak Supervision. - Jeffrey Li; Jieyu Zhang 001; Ludwig Schmidt; Alexander J. Ratner (NeurIPS, 2023)

Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias. - Yue Yu 001; Yuchen Zhuang; Jieyu Zhang 001; Yu Meng 001; Alexander J. Ratner; Ranjay Krishna; Jiaming Shen; Chao Zhang 014 (NeurIPS, 2023)

On the Trade-off of Intra-/Inter-class Diversity for Supervised Pre-training. - Jieyu Zhang 001; Bohan Wang; Zhengyu Hu; Pang Wei Koh; Alexander J. Ratner (NeurIPS, 2023)

Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models. - Cheng-Yu Hsieh; Si-An Chen; Chun-Liang Li; Yasuhisa Fujii; Alexander Ratner; Chen-Yu Lee; Ranjay Krishna; Tomas Pfister (CoRR, 2023)

Found in the middle: Calibrating Positional Attention Bias Improves Long Context Utilization. - Cheng-Yu Hsieh; Yung-Sung Chuang; Chun-Liang Li; Zifeng Wang 002; Long T. Le; Abhishek Kumar; James R. Glass; Alexander Ratner; Chen-Yu Lee; Ranjay Krishna; Tomas Pfister (ACL (Findings), 2024)

Language Model Preference Evaluation with Multiple Weak Evaluators. - Zhengyu Hu; Jieyu Zhang 001; Zhihan Xiong; Alexander Ratner; Hui Xiong 001; Ranjay Krishna (CoRR, 2024)

MaskSearch: Querying Image Masks at Scale. - Dong He 002; Jieyu Zhang 001; Maureen Daum; Alexander Ratner; Magdalena Balazinska (ICDE, 2025)