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WeaSuL: Workshop on Weakly Supervised Learning @ ICLR 2021
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May 8, 2021, Virtual
https://weasul.github.io
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ABOUT THE WORKSHOP
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Deep learning relies on massive training sets of labeled examples to learn from - often tens of thousands to millions to reach peak predictive performance. However, large amounts of training data are only available for very few standardized learning problems. Even small variations of the problem specification or changes in the data distribution would necessitate re-annotation of large amounts of data.
However, domain knowledge can often be expressed by sets of prototypical descriptions: For example, vision experts can exploit meta information for image labeling, linguists can describe discourse phenomena by prototypical realization patterns, social scientists can specify events of interest by characteristic key phrases, and bio-medical researchers have databases of known interactions between drugs or proteins that can be used for heuristic labeling. These knowledge-based descriptions can be either used as rule-based predictors or as labeling functions for providing partial data annotations. The growing field of weak supervision provides methods for refining and generalizing such heuristic-based annotations in interaction with deep neural networks and large amounts of unannotated data. In this workshop, we want to advance theory, methods and tools for allowing experts to express prior coded knowledge for automatic data annotations that can be used to train arbitrary deep neural networks for prediction.
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TOPICS OF INTEREST
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Topics of interest include, but are not limited to:
- Weak supervision in combination with neural networks and representation learning
- Theoretic insights into weak supervision
- Relationship between weak supervision and other machine learning paradigms incl. semi-supervised learning, active learning and label denoising
- Distant supervision and weak supervision for specific tasks
- Interdisciplinary applications of weak supervision
- Unification of weak supervision approaches from different fields, e.g, relation extraction (natural language processing) and image classification (vision)
- Analysis of failure cases of weak supervision
- Benchmarks for evaluating and comparing weak supervision approaches
- Applications of weak supervision in industry settings
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IMPORTANT DATES
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Feb 26, 2021: Paper Submission Deadline
Mar 26, 2021: Author Notification
May 8, 2021: Workshop Date
All deadlines are calculated at 11:59pm Pacific Daylight Savings Time (UTC - 12h, Everywhere on Earth)
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INVITED SPEAKERS
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- Dan Roth, University of Pennsylvania
- Paroma Varma, Snorkel AI
- Heng Ji, University of Illinois
- Lu Jiang, Google Research
- Marine Carpuat, University of Maryland
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SUBMISSION INSTRUCTIONS
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We solicit two categories of papers: long and short papers. Authors can decide the archival status of their publications. Submissions will go through a double-blind review process, where each submission is reviewed by at least two program committee members.
Accepted papers will be presented by the authors either as talk or poster. All submissions must follow the ICLR 2021 formatting requirements (https://iclr.cc/Conferences/2021/CallForPapers).
- Short paper submission: up to 4 pages of content (+1 on acceptance), plus bibliography
- Long paper submission: up to 8 pages of content (+1 on acceptance), plus bibliography
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CONTACTS
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The co-chairs of the workshop can be contacted by email at:
weaksupervision (at) googlegroups.com
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ORGANIZING COMMITTEE
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- Benjamin Roth, University of Vienna
- Barbara Plank, IT University of Copenhagen
- Alex Ratner, University of Washington
- Katharina Kann, University of Colorado Boulder
- Dietrich Klakow, Saarland University
- Michael A. Hedderich, Saarland University