786 citations · 2k across the 26 of their papers we have counts for
16 papers · 1 filter
On the Trade-off of Intra-/Inter-class Diversity for Supervised Pre-training
Jieyu Zhang, Bohan Wang, Zhengyu Hu +2
Pre-training datasets are critical for building state-of-the-art machine learning models, motivating rigorous study on their impact on downstream tasks. In this work, we study the…
Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision
Jieyu Zhang, Linxin Song, Alexander Ratner
Programmatic Weak Supervision (PWS) has emerged as a widespread paradigm to synthesize training labels efficiently. The core component of PWS is the label model, which infers true…
Binary Classification with Positive Labeling Sources
Jieyu Zhang, Yujing Wang, Yaming Yang +2
To create a large amount of training labels for machine learning models effectively and efficiently, researchers have turned to Weak Supervision (WS), which uses programmatic label…
Understanding Programmatic Weak Supervision via Source-aware Influence Function
Jieyu Zhang, Haonan Wang, Cheng-Yu Hsieh +1
Programmatic Weak Supervision (PWS) aggregates the source votes of multiple weak supervision sources into probabilistic training labels, which are in turn used to train an end mode…
Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming
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. While the succes…
A Survey on Programmatic Weak Supervision
Jieyu Zhang, Cheng-Yu Hsieh, Yue Yu +2
Labeling training data has become one of the major roadblocks to using machine learning. Among various weak supervision paradigms, programmatic weak supervision (PWS) has achieved…
WRENCH: A Comprehensive Benchmark for Weak Supervision
Jieyu Zhang, Yue Yu, Yinghao Li +4
Recent Weak Supervision (WS) approaches have had widespread success in easing the bottleneck of labeling training data for machine learning by synthesizing labels from multiple pot…
Creating Training Sets via Weak Indirect Supervision
Jieyu Zhang, Bohan Wang, Xiangchen Song +4
Creating labeled training sets has become one of the major roadblocks in machine learning. To address this, recent \emph{Weak Supervision (WS)} frameworks synthesize training label…
Proceedings of the First Workshop on Weakly Supervised Learning (WeaSuL)
Michael A. Hedderich, Benjamin Roth, Katharina Kann +3
Welcome to WeaSuL 2021, the First Workshop on Weakly Supervised Learning, co-located with ICLR 2021. In this workshop, we want to advance theory, methods and tools for allowing exp…
Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices
Vincent S. Chen, Sen Wu, Zhenzhen Weng +2
In real-world machine learning applications, data subsets correspond to especially critical outcomes: vulnerable cyclist detections are safety-critical in an autonomous driving tas…
MLSys: The New Frontier of Machine Learning Systems
Alexander Ratner, Dan Alistarh, Gustavo Alonso +66
Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains…
Cross-Modal Data Programming Enables Rapid Medical Machine Learning
Jared Dunnmon, Alexander Ratner, Nishith Khandwala +8
Labeling training datasets has become a key barrier to building medical machine learning models. One strategy is to generate training labels programmatically, for example by applyi…
Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale
Stephen H. Bach, Daniel Rodriguez, Yintao Liu +10
Labeling training data is one of the most costly bottlenecks in developing machine learning-based applications. We present a first-of-its-kind study showing how existing knowledge…
A Kernel Theory of Modern Data Augmentation
Tri Dao, Albert Gu, Alexander J. Ratner +3
Data augmentation, a technique in which a training set is expanded with class-preserving transformations, is ubiquitous in modern machine learning pipelines. In this paper, we seek…
Snorkel: Rapid Training Data Creation with Weak Supervision
Alexander Ratner, Stephen H. Bach, Henry Ehrenberg +3
Labeling training data is increasingly the largest bottleneck in deploying machine learning systems. We present Snorkel, a first-of-its-kind system that enables users to train stat…
Learning the Structure of Generative Models without Labeled Data
Stephen H. Bach, Bryan He, Alexander Ratner +1
Curating labeled training data has become the primary bottleneck in machine learning. Recent frameworks address this bottleneck with generative models to synthesize labels at scale…