TAGLETS: A System for Automatic Semi-Supervised Learning with Auxiliary Data
This paper describes TAGLETS, a system built to study techniques for automatically exploiting all three types of data and creating high-quality, servable classifiers
Ontology-driven weak supervision for clinical entity classiﬁcation in electronic health records
Presenting Trove, a framework for weakly supervised entity classification using medical ontologies and expert-generated rules.
Universalizing Weak Supervision
This paper proposes a universal technique that enables weak supervision over any label type while still offering desirable properties, including practical flexibility, computational efficiency, and theoretical guarantees.
Multitask prompted training enables zero-shot task generalization
This paper showcases how using a data-centric approach to generate high-quality training data at massive scale to improve the zero-shot abilities of that model.
Creating Training Sets via Weak Indirect Supervision
This paper extends the scope of usable sources in WS, by formulating Weak Indirect Supervision (WIS), a new research problem for automatically synthesizing training labels based on indirect supervision sources that have different output label spaces.
Efficiently Modeling Long Sequences with Structured State Spaces
This paper introduces the Structured State Space sequence model (s4), which uses a new parameterization for the state-space model to improve long-range dependency handling both mathematically and empirically.
Learning from Multiple Noisy Partial Labelers
This work enables users to create partial labelers that output subsets of possible class labels would greatly expand the expressivity of programmatic weak supervision.
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.