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ORCIDAlexander Ratner
Alex Ratner – Alexander J. Ratner
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2020 – today
- 2024
[c26]Cheng-Yu Hsieh, Yung-Sung Chuang, Chun-Liang Li, Zifeng Wang, Long T. Le, Abhishek Kumar, James R. Glass, Alexander Ratner, Chen-Yu Lee, Ranjay Krishna, Tomas Pfister:
Found in the middle: Calibrating Positional Attention Bias Improves Long Context Utilization. ACL (Findings) 2024: 14982-14995- [i27]Cheng-Yu Hsieh, Yung-Sung Chuang, Chun-Liang Li, Zifeng Wang, Long T. Le, Abhishek Kumar, James R. Glass, Alexander Ratner, Chen-Yu Lee, Ranjay Krishna, Tomas Pfister:
Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization. CoRR abs/2406.16008 (2024) - 2023
- [c25]Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alex Ratner, Ranjay Krishna, Chen-Yu Lee, Tomas Pfister:
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes. ACL (Findings) 2023: 8003-8017 - [c24]Jieyu Zhang, Linxin Song, Alex Ratner:
Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision. AISTATS 2023: 157-171 - [c23]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 - [c22]Jeffrey Li, Jieyu Zhang, Ludwig Schmidt, Alexander J. Ratner:
Characterizing the Impacts of Semi-supervised Learning for Weak Supervision. NeurIPS 2023 - [c21]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 - [c20]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 - [i26]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 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. CoRR abs/2304.14108 (2023) - [i25]Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, Tomas Pfister:
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes. CoRR abs/2305.02301 (2023) - [i24]Dong He, Jieyu Zhang, Maureen Daum, Alexander Ratner, Magdalena Balazinska:
MaskSearch: Querying Image Masks at Scale. CoRR abs/2305.02375 (2023) - [i23]Jieyu Zhang, Bohan Wang, Zhengyu Hu, Pang Wei Koh, Alexander Ratner:
On the Trade-off of Intra-/Inter-class Diversity for Supervised Pre-training. CoRR abs/2305.12224 (2023) - [i22]Yue Yu, Yuchen Zhuang, Jieyu Zhang, Yu Meng, Alexander Ratner, Ranjay Krishna, Jiaming Shen, Chao Zhang:
Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias. CoRR abs/2306.15895 (2023) - [i21]Cheng-Yu Hsieh, Si-An Chen, Chun-Liang Li, Yasuhisa Fujii, Alexander Ratner, Chen-Yu Lee, Ranjay Krishna, Tomas Pfister:
Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models. CoRR abs/2308.00675 (2023) - 2022
- [j9]Cheng-Yu Hsieh, Jieyu Zhang, Alexander J. Ratner:
Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming. Proc. VLDB Endow. 15(13): 4093-4105 (2022) - [c19]Jieyu Zhang, Yujing Wang, Yaming Yang, Yang Luo, Alexander Ratner:
Binary Classification with Positive Labeling Sources. CIKM 2022: 4672-4676 - [c18]Jieyu Zhang, Bohan Wang, Xiangchen Song, Yujing Wang, Yaming Yang, Jing Bai, Alexander Ratner:
Creating Training Sets via Weak Indirect Supervision. ICLR 2022 - [c17]Jieyu Zhang, Haonan Wang, Cheng-Yu Hsieh, Alexander J. Ratner:
Understanding Programmatic Weak Supervision via Source-aware Influence Function. NeurIPS 2022 - [i20]Jieyu Zhang, Cheng-Yu Hsieh, Yue Yu, Chao Zhang, Alexander Ratner:
A Survey on Programmatic Weak Supervision. CoRR abs/2202.05433 (2022) - [i19]Cheng-Yu Hsieh, Jieyu Zhang, Alexander Ratner:
Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming. CoRR abs/2203.01382 (2022) - [i18]Jieyu Zhang, Haonan Wang, Cheng-Yu Hsieh, Alexander Ratner:
Understanding Programmatic Weak Supervision via Source-aware Influence Function. CoRR abs/2205.12879 (2022) - [i17]Jieyu Zhang, Yujing Wang, Yaming Yang, Yang Luo, Alexander Ratner:
Binary Classification with Positive Labeling Sources. CoRR abs/2208.01704 (2022) - [i16]Jieyu Zhang, Linxin Song, Alexander Ratner:
Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision. CoRR abs/2210.02724 (2022) - 2021
- [c16]Jieyu Zhang, Yue Yu, Yinghao Li, Yujing Wang, Yaming Yang, Mao Yang, Alexander Ratner:
WRENCH: A Comprehensive Benchmark for Weak Supervision. NeurIPS Datasets and Benchmarks 2021 - [i15]Michael A. Hedderich, Benjamin Roth, Katharina Kann, Barbara Plank, Alex Ratner, Dietrich Klakow:
Proceedings of the First Workshop on Weakly Supervised Learning (WeaSuL). CoRR abs/2107.03690 (2021) - [i14]Jieyu Zhang, Yue Yu, Yinghao Li, Yujing Wang, Yaming Yang, Mao Yang, Alexander Ratner:
WRENCH: A Comprehensive Benchmark for Weak Supervision. CoRR abs/2109.11377 (2021) - [i13]Jieyu Zhang, Bohan Wang, Xiangchen Song, Yujing Wang, Yaming Yang, Jing Bai, Alexander Ratner:
Creating Training Sets via Weak Indirect Supervision. CoRR abs/2110.03484 (2021) - 2020
- [j8]Emily K. Mallory, Matthieu de Rochemonteix, Alexander Ratner, Ambika Acharya, Christopher Ré, Roselie A. Bright, Russ B. Altman
:
Extracting chemical reactions from text using Snorkel. BMC Bioinform. 21(1): 217 (2020) - [j7]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): 100019 (2020) - [j6]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)
2010 – 2019
- 2019
- [b1]Alexander Ratner:
Accelerating machine learning with training data management. Stanford University, USA, 2019 - [c15]Alexander Ratner, Braden Hancock, Jared Dunnmon, Frederic Sala, Shreyash Pandey, Christopher Ré:
Training Complex Models with Multi-Task Weak Supervision. AAAI 2019: 4763-4771 - [c14]Alexander J. Ratner, Braden Hancock, Christopher Ré:
The Role of Massively Multi-Task and Weak Supervision in Software 2.0. CIDR 2019 - [c13]Tri Dao, Albert Gu, Alexander Ratner, Virginia Smith, Chris De Sa, Christopher Ré:
A Kernel Theory of Modern Data Augmentation. ICML 2019: 1528-1537 - [c12]Paroma Varma, Frederic Sala, Ann He, Alexander Ratner, Christopher Ré:
Learning Dependency Structures for Weak Supervision Models. ICML 2019: 6418-6427 - [c11]Khaled Saab, Jared Dunnmon, Roger E. Goldman, Alexander Ratner, Hersh Sagreiya, Christopher Ré, Daniel L. Rubin:
Doubly Weak Supervision of Deep Learning Models for Head CT. MICCAI (3) 2019: 811-819 - [c10]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: 9392-9402 - [c9]Eran Bringer, Abraham Israeli, Yoav Shoham, Alexander Ratner, Christopher Ré:
Osprey: Weak Supervision of Imbalanced Extraction Problems without Code. DEEM@SIGMOD 2019: 4:1-4:11 - [c8]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 - [i12]Paroma Varma, Frederic Sala, Ann He, Alexander Ratner, Christopher Ré:
Learning Dependency Structures for Weak Supervision Models. CoRR abs/1903.05844 (2019) - [i11]Jared Dunnmon, Alexander Ratner, Nishith Khandwala, Khaled Saab, 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. CoRR abs/1903.11101 (2019) - [i10]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, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Randall Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar:
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i9]Vincent S. Chen, Sen Wu, Zhenzhen Weng, Alexander Ratner, Christopher Ré:
Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices. CoRR abs/1909.06349 (2019) - 2018
- [j5]Alexander Ratner, Christopher Ré, Peter Bailis:
Research for practice: knowledge base construction in the machine-learning era. Commun. ACM 61(11): 95-97 (2018) - [j4]Alexander Ratner, Christopher Ré:
Knowledge Base Construction in the Machine-learning Era. ACM Queue 16(3): 50 (2018) - [c7]Alexander Ratner, Braden Hancock, Jared Dunnmon, Roger E. Goldman, Christopher Ré:
Snorkel MeTaL: Weak Supervision for Multi-Task Learning. DEEM@SIGMOD 2018: 3:1-3:4 - [i8]Tri Dao, Albert Gu, Alexander J. Ratner, Virginia Smith, Christopher De Sa, Christopher Ré:
A Kernel Theory of Modern Data Augmentation. CoRR abs/1803.06084 (2018) - [i7]Alexander Ratner, Braden Hancock, Jared Dunnmon, Frederic Sala, Shreyash Pandey, Christopher Ré:
Training Complex Models with Multi-Task Weak Supervision. CoRR abs/1810.02840 (2018) - [i6]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
- [j3]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) - [j2]Christopher De Sa, Alexander Ratner, Christopher Ré, Jaeho Shin, Feiran Wang, Sen Wu, Ce Zhang:
Incremental knowledge base construction using DeepDive. VLDB J. 26(1): 81-105 (2017) - [c6]Alexander Ratner, Stephen H. Bach, Henry R. Ehrenberg, Jason Alan Fries, Sen Wu, Christopher Ré:
Snorkel: A System for Lightweight Extraction. CIDR 2017 - [c5]Stephen H. Bach, Bryan Dawei He, Alexander Ratner, Christopher Ré:
Learning the Structure of Generative Models without Labeled Data. ICML 2017: 273-282 - [c4]Alexander J. Ratner, Henry R. Ehrenberg, Zeshan Hussain, Jared Dunnmon, Christopher Ré:
Learning to Compose Domain-Specific Transformations for Data Augmentation. NIPS 2017: 3236-3246 - [c3]Alexander J. Ratner, Stephen H. Bach, Henry R. Ehrenberg, Christopher Ré:
Snorkel: Fast Training Set Generation for Information Extraction. SIGMOD Conference 2017: 1683-1686 - [i5]Stephen H. Bach, Bryan Dawei He, Alexander Ratner, Christopher Ré:
Learning the Structure of Generative Models without Labeled Data. CoRR abs/1703.00854 (2017) - [i4]Jason A. Fries, Sen Wu, Alexander Ratner, Christopher Ré:
SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data. CoRR abs/1704.06360 (2017) - [i3]Alexander J. Ratner, Henry R. Ehrenberg, Zeshan Hussain, Jared Dunnmon, Christopher Ré:
Learning to Compose Domain-Specific Transformations for Data Augmentation. CoRR abs/1709.01643 (2017) - [i2]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) - 2016
- [j1]Christopher De Sa, Alexander Ratner, Christopher Ré, Jaeho Shin, Feiran Wang, Sen Wu, Ce Zhang:
DeepDive: Declarative Knowledge Base Construction. SIGMOD Rec. 45(1): 60-67 (2016) - [c2]Alexander J. Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, Christopher Ré:
Data Programming: Creating Large Training Sets, Quickly. NIPS 2016: 3567-3575 - [c1]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: 13 - [i1]Alexander Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, Christopher Ré:
Data Programming: Creating Large Training Sets, Quickly. CoRR abs/1605.07723 (2016)
Coauthor Index
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last updated on 2024-09-26 00:59 CEST by the dblp team
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