Amazon Science explains Hamed Karimi's 2016 optimization paper and its influence
Overview
Amazon Science says senior applied scientist Hamed Karimi and colleagues' 2016 paper on linear convergence under the Polyak–Łojasiewicz condition won the 2026 ECML PKDD Test-of-Time Award.
The paper, written with Julie Nutini and Mark Schmidt while Karimi was a postdoctoral researcher at the University of British Columbia, uses a 1963 inequality to show that standard machine learning training methods converge quickly even when many solutions work equally well.
Amazon Science says the paper helped organize a previously scattered field of machine learning optimization and offers approaches for analyzing new training methods.
Written by AI from the articles below · updated Nov 5, 10:07 AM ET
Check the sources:
Article timeline
The articles in this story. Times are ET.
- Amazon ScienceOfficialHamed Karimi wins ECML PKDD Test-of-Time Award for 2016 optimization paper
AIAmazon senior applied scientist Hamed Karimi and colleagues won the 2026 ECML PKDD Test-of-Time Award for their 2016 paper on linear convergence under the Polyak–Łojasiewicz condition. The paper showed that a 1963 inequality can prove that standard machine learning training methods converge quickly even when many different solutions work equally well.
Heat Trend
Not enough continuous observations to show a trend yet.