Alexander J. Ratner

Publication Activity (10 Years)

Years Active: 2016-2023
Publications (10 Years): 15

Top Topics

Supervised Learning

Top Venues

Proc. VLDB Endow.

Publications

Jeffrey Li, Jieyu Zhang, Ludwig Schmidt, Alexander J. Ratner
Characterizing the Impacts of Semi-supervised Learning for Weak Supervision. NeurIPS (2023)
Jieyu Zhang, Bohan Wang, Zhengyu Hu, Pang Wei Koh, Alexander J. Ratner
On the Trade-off of Intra-/Inter-class Diversity for Supervised Pre-training. NeurIPS (2023)
Yue Yu, Yuchen Zhuang, Jieyu Zhang, Yu Meng, Alexander J. Ratner, Ranjay Krishna, Jiaming Shen, Chao Zhang
Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias. NeurIPS (2023)
Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, Eyal Orgad, Rahim Entezari, Giannis Daras, Sarah M. Pratt, Vivek Ramanujan, Yonatan Bitton, Kalyani Marathe, Stephen Mussmann, Richard Vencu, Mehdi Cherti, Ranjay Krishna, Pang Wei Koh, Olga Saukh, Alexander J. Ratner, Shuran Song, Hannaneh Hajishirzi, Ali Farhadi, Romain Beaumont, Sewoong Oh, Alex Dimakis, Jenia Jitsev, Yair Carmon, Vaishaal Shankar, Ludwig Schmidt
DataComp: In search of the next generation of multimodal datasets. NeurIPS (2023)
Cheng-Yu Hsieh, Jieyu Zhang, Alexander J. Ratner
Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming. Proc. VLDB Endow. 15 (13) (2022)
Jieyu Zhang, Haonan Wang, Cheng-Yu Hsieh, Alexander J. Ratner
Understanding Programmatic Weak Supervision via Source-aware Influence Function. NeurIPS (2022)
Jared A. Dunnmon, Alexander J. Ratner, Khaled Saab, Nishith Khandwala, Matthew Markert, Hersh Sagreiya, Roger E. Goldman, Christopher Lee-Messer, Matthew P. Lungren, Daniel L. Rubin, Christopher Ré
Cross-Modal Data Programming Enables Rapid Medical Machine Learning. Patterns 1 (2) (2020)
Alexander J. Ratner, Braden Hancock, Christopher Ré
The Role of Massively Multi-Task and Weak Supervision in Software 2.0. CIDR (2019)
Vincent S. Chen, Sen Wu, Alexander J. Ratner, Jen Weng, Christopher Ré
Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices. NeurIPS (2019)
Tri Dao, Albert Gu, Alexander J. Ratner, Virginia Smith, Christopher De Sa, Christopher Ré
A Kernel Theory of Modern Data Augmentation. CoRR (2018)
Alexander J. Ratner, Stephen H. Bach, Henry R. Ehrenberg, Christopher Ré
Snorkel: Fast Training Set Generation for Information Extraction. SIGMOD Conference (2017)
Alexander J. Ratner, Henry R. Ehrenberg, Zeshan Hussain, Jared Dunnmon, Christopher Ré
Learning to Compose Domain-Specific Transformations for Data Augmentation. NIPS (2017)
Alexander J. Ratner, Henry R. Ehrenberg, Zeshan Hussain, Jared Dunnmon, Christopher Ré
Learning to Compose Domain-Specific Transformations for Data Augmentation. CoRR (2017)
Henry R. Ehrenberg, Jaeho Shin, Alexander J. Ratner, Jason A. Fries, Christopher Ré
Data programming with DDLite: putting humans in a different part of the loop. HILDA@SIGMOD (2016)
Alexander J. Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, Christopher Ré
Data Programming: Creating Large Training Sets, Quickly. NIPS (2016)