18.07.2025

Outstanding Paper Award at ICML 2025 for MCML Researchers

Unai Fischer Abaigar and Christoph Kern Honored for Their Paper on Identifying the Worst-Off Through Prediction

We are proud to share that the paper “The Value of Prediction in Identifying the Worst-Off” by our Junior Member Unai Fischer Abaigar, and Associate Christoph Kern, and collaborator Juan Carlos Perdomo from Harvard University has been selected for an Outstanding Paper Award at ICML 2025.

Only six papers received this prestigious recognition this year, highlighting the exceptional quality and impact of their work.

Congrats from us!

Check out the full paper:

U. Fischer Abaigar, C. Kern and J. Perdomo.
The Value of Prediction in Identifying the Worst-Off.
ICML 2025 - 42nd International Conference on Machine Learning. Vancouver, Canada, Jul 13-19, 2025. Spotlight Presentation. Outstanding Paper Award. To be published. Preprint available. arXiv

Abstract

Machine learning is increasingly used in government programs to identify and support the most vulnerable individuals, prioritizing assistance for those at greatest risk over optimizing aggregate outcomes. This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic capacity. Through mathematical models and a real-world case study on long-term unemployment amongst German residents, we develop a comprehensive understanding of the relative effectiveness of prediction in surfacing the worst-off. Our findings provide clear analytical frameworks and practical, data-driven tools that empower policymakers to make principled decisions when designing these systems.

MCML Authors