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This award recognizes foundational theoretical work demonstrating how a 1963 mathematical condition validates the convergence of modern machine learning optimization methods. By bridging the gap between practical training behavior and theoretical guarantees, this research solidifies the mathematical underpinnings of gradient-based algorithms used to train large-scale models today.
Hamed Karimi wins ECML PKDD Test-of-Time Award
Amazon senior applied scientist and coauthors showed that an overlooked mathematical condition from 1963 proves why frequently used machine-learning optimization methods work well.
In Naples last month, at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Amazon senior applied scientist Hamed Karimi and colleagues won the 2026 ECML PKDD Test-of-Time Award for their 2016 paper “Linear convergence of gradient and proximal-gradient methods under the Polyak–Łojasiewicz condition”. The award recognizes research published a decade earlier that has had a lasting impact. The paper, written with Julie Nutini and Mark Schmidt while Karimi was a postdoctoral researcher at the University of British Columbia, has helped to organize a previously scattered field of machine learning optimization and offers useful approaches to analyzing new training methods. “When we wrote the paper in 2016, we genuinely didn’t expect it…
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