Workshop

WeaSuL 2021


Workshop on Weakly Supervised Learning

ICLR 2021 Workshop
May 7 2021
virtual


Call for Papers

Important Dates

Paper Submission Deadline: Feb 26, 2021

Author Notification: Mar 26, 2021

Workshop date: May 7, 2021

Topics of Interest

We invite submissions on (but not limited to) the following topics:

  • 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


Submission Instructions

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)

  • Long paper submission: up to 8 pages of content (+1 on acceptance), plus bibliography
  • Short paper submission: up to 4 pages of content(+1 on acceptance), plus bibliography

Papers being submitted both to WeaSuL and another conference or workshop must note in the submission form the other conference or workshop. If the paper is accepted for the other venue, the authors must contact the WeaSuL organizers (archival publications are only possible if the paper is not published elsewhere).

Submission via CMT: https://cmt3.research.microsoft.com/WEASUL2021