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About
About
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.
ML-IRL will be held at ICLR 2020 online on April 26, 2020. See the updated program for details. The workshop will run from 13:00-21:00 BST (GMT+1).
keynote speakers
keynote speakers
Johns Hopkins University
Harvard University
Google AI Lab Ghana
commitment to diversity
commitment to diversity
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.
Congratulations to our registration award winners:
- Amsale Zelalem, Addis Ababa University (Ethiopia)
- Ismaël Koné, Moulay Ismail University (Morocco)
- Wiebke Toussaint, Delft University of Technology/University of Cape Town (Netherlands/South Africa)
- Yaecob Girmay Gezahegn, Mekelle Institute of Technology-Mekelle University (Ethiopia)
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