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Rumi Chunara <[log in to unmask]>
Mon, 20 Jan 2020 23:32:21 +0500
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ICLR 2020 Workshop Machine Learning in Real-Life (ML-IRL)

Workshop at International Conference on Learning Representations

Sunday April 26, 2020

Addis Ababa, Ethiopia

Key dates:

* January 25, 2020: Submission deadline

* February 25, 2020: Acceptance notification

* April 26, 2020: Workshop


ML-IRL is about the challenges of real-world use of machine learning and
the gap between what ML can do in theory and what is needed in practice.
Given the tremendous recent advances in methodology from causal inference
to deep learning, the strong interest in applications (in health, climate
and beyond), and discovery of problematic implications (e.g. issues of
fairness and explainability) now is an ideal time to examine how we
develop, evaluate and deploy ML and how we can do it better. We envision a
workshop that is focused on productive solutions, not mere identification of
problems or demonstration of failures.


We believe one of the keys to making ML that really works is involving a
diverse set of people and perspectives in its development, deployment, and
evaluation. Our program committee spans academia and industry across four
continents and has experience ranging from theoretical machine learning to
legal implications of AI. We welcome all submissions that share our goal of
ML in IRL, and especially encourage submissions from researchers who may
not regularly attend ICLR or other ML conferences. We will have a limited
number of free guaranteed registrations available for local students and
researchers. To apply, visit:
Applications are due February 26, 2020. Applications after this date will
be considered if awards are still available.


*Andreas Gros, Facebook

*Nyalleng, Moorosi, Google AI Lab Ghana

*Susan Murphy, Harvard University

*Suchi Saria, Johns Hopkins

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

  -Data collection and algorithms designed for the challenges of real-world
  -Causal inference with realistic assumptions
  -Transportability across contexts and domains
 -Observational data and deep learning
  -Grand challenges and blind spots for specific domains
  -How to avoid failures
  -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!

*Short papers and position pieces (4 pages, with unlimited appendices)
*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.

Contributions should be blinded and submitted using the ICLR template via


Samantha Kleinberg (Stevens Institute of Technology)

Rumi Chunara (New York University)
Rumi Chunara, PhD
Assistant Professor
NYU Computer Science & Engineering
NYU College of Global Public Health

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