Search Results for author: Alexander Ratner

Found 14 papers, 6 papers with code

Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming

no code implementations2 Mar 2022 Cheng-Yu Hsieh, Jieyu Zhang, Alexander Ratner

Weak Supervision (WS) techniques allow users to efficiently create large training datasets by programmatically labeling data with heuristic sources of supervision.

A Survey on Programmatic Weak Supervision

1 code implementation11 Feb 2022 Jieyu Zhang, Cheng-Yu Hsieh, Yue Yu, Chao Zhang, Alexander Ratner

Labeling training data has become one of the major roadblocks to using machine learning.

WRENCH: A Comprehensive Benchmark for Weak Supervision

1 code implementation23 Sep 2021 Jieyu Zhang, Yue Yu, Yinghao Li, Yujing Wang, Yaming Yang, Mao Yang, Alexander Ratner

To address these problems, we introduce a benchmark platform, WRENCH, for thorough and standardized evaluation of WS approaches.

Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices

2 code implementations NeurIPS 2019 Vincent S. Chen, Sen Wu, Zhenzhen Weng, Alexander Ratner, Christopher Ré

In real-world machine learning applications, data subsets correspond to especially critical outcomes: vulnerable cyclist detections are safety-critical in an autonomous driving task, and "question" sentences might be important to a dialogue agent's language understanding for product purposes.

Autonomous Driving

Improving Sample Complexity with Observational Supervision

no code implementations ICLR Workshop LLD 2019 Khaled Saab, Jared Dunnmon, Alexander Ratner, Daniel Rubin, Christopher Re

Supervised machine learning models for high-value computer vision applications such as medical image classification often require large datasets labeled by domain experts, which are slow to collect, expensive to maintain, and static with respect to changes in the data distribution.

Image Classification

Snorkel: Rapid Training Data Creation with Weak Supervision

2 code implementations28 Nov 2017 Alexander Ratner, Stephen H. Bach, Henry Ehrenberg, Jason Fries, Sen Wu, Christopher Ré

In a user study, subject matter experts build models 2. 8x faster and increase predictive performance an average 45. 5% versus seven hours of hand labeling.

Data Programming: Creating Large Training Sets, Quickly

3 code implementations NeurIPS 2016 Alexander Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, Christopher Ré

Additionally, in initial user studies we observed that data programming may be an easier way for non-experts to create machine learning models when training data is limited or unavailable.

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