Ordinal Programmatic Weak Supervision and Crowdsourcing for Estimating Cognitive States (Student Abstract)

Authors

  • Prakruthi Pradeep Carnegie Mellon University
  • Benedikt Boecking Carnegie Mellon University
  • Nicholas Gisolfi Carnegie Mellon University
  • Jacob R. Kintz The University of Colorado Boulder
  • Torin K. Clark The University of Colorado Boulder
  • Artur Dubrawski Carnegie Mellon University

DOI:

https://doi.org/10.1609/aaai.v37i13.27012

Keywords:

Crowdsourcing, Weak Supervision, Ordinal Classification

Abstract

Crowdsourcing and weak supervision offer methods to efficiently label large datasets. Our work builds on existing weak supervision models to accommodate ordinal target classes, in an effort to recover ground truth from weak, external labels. We define a parameterized factor function and show that our approach improves over other baselines.
AAAI-23 Proceedings Cover

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Published

2024-07-15

How to Cite

Pradeep, P., Boecking, B., Gisolfi, N., Kintz, J. R., Clark, T. K., & Dubrawski, A. (2024). Ordinal Programmatic Weak Supervision and Crowdsourcing for Estimating Cognitive States (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16304–16305. https://doi.org/10.1609/aaai.v37i13.27012

Issue

Vol. 37 No. 13: AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations

Section

AAAI Student Abstract and Poster Program