Semi-Supervised Aggregation of Dependent Weak Supervision Sources with Performance Guarantees
This work shows a rigorous technique for efficiently selecting small subsets of the labelers so that a majority vote from such subsets has a provably low error rate.
Adversarial Multiclass Learning under Weak Supervision with Performance Guarantees
This paper presents a rigorous approach for using a set of arbitrarily correlated weak supervision sources in order to solve a multiclass classification task when only a very small set of labeled data is available
Reference-based Weak Supervision for Answer Sentence Selection using Web Data
This work showcases the Reference-based Weak Supervision (RWS), a fully automatic large-scale data pipeline that harvests high-quality weakly- supervised answer sentences from Web data, only requiring a question-reference pair as input.
WRENCH: A Comprehensive Benchmark for Weak Supervision
This paper introduces a benchmark platform, WRENCH, for thorough and standardized evaluation of WS approaches.
Leveraging Organizational Resources to Adapt Models to New Data Modalities
This work demonstrates how organizational resources, in the form of aggregate statistics, knowledge bases, and existing services can be used to connect new and existing data modalities.
Fast and Three-Rious: Speed up Weak Supervision With Triplet Methods
Introducing FlyingSquid, a weak supervision framework that runs orders of magnitude faster than previous weak supervision approaches and requires fewer assumptions
Cross-Modal Data Programming Enables Rapid Medical Machine Learning
This paper proposes cross-modal data programming (XMDP) for machine learning (ML) in medicine.
Weakly Supervised Classification of Aortic Valve Malformations Using Unlabeled Cardiac MRI Sequences
This work formalizes a deep learning baseline for aortic valve classification and outlines a general strategy for using weak supervision to train machine learning models using unlabeled medical images at scale.