Call for papers

Topics

We aim to examine how real-world applications can and should influence every stage of ML, from how we develop algorithms to how we evaluate them. The key themes of the workshop are foundational challenges that are domain independent:

• Methods
  • Data collection and algorithms designed for the challenges of real-world data
  • Causal inference with realistic assumptions
  • Transportability across contexts and domains
  • Observational data and deep learning
• Applications
  • Grand challenges and blindspots for specific domains
  • How to avoid failures
• Implications
  • How can we build properties like fairness in from the start?
  • The gap between what is possible (in terms of data/law/society) and common methodological assumptions
  • Human and ML interaction and collaboration, and considering humans in ML development more broadly

This list is not exhaustive and we both welcome and encourage submissions on all aspects of machine learning in real-life!

Instructions

Submission types

• Short papers and position pieces (4 pages, unlimited appendices are allowed)
• Problem statements and abstracts (1 page)

4-page submissions will be eligible for oral or poster presentation. One page submissions will be presented as posters. As the workshop is non-archival, we allow submission of papers which are under review at other locations currently or already published. The idea being to bring together the most pertinent topics and conversation in this area.

Submission format

Contributions should be blinded and submitted using the ICLR template via CMT: https://cmt3.research.microsoft.com/MLIRL2020/

Important dates

Submission deadline January 25, 2020 (11:59pm AoE)

Notification February 25, 2020

Camera ready March 25, 2020

Workshop April 26, 2020