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Amazon senior applied scientists Hamed Karimi, Julie Nutini, and Mark Schmidt have received 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 paper proved that the Polyak–Łojasiewicz condition, long overlooked by the machine learning community, can guarantee that gradient descent converges at a linear rate, and it clarified the relationships among previously scattered weakly convex optimization methods. The paper has been cited more than 2,000 times and has been included in graduate textbooks.
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Hamed Karimi and others receive the ECML PKDD 2026 Test-of-Time Award
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Hamed Karimi Receives ECML PKDD Test-of-Time Award, 2016...
Amazon senior applied scientist Hamed Karimi, along with Julie Nutini and Mark Schmidt, received 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 paper proved that the Polyak–Łojasiewicz condition, long overlooked by the machine learning community, can guarantee that gradient descent converges at a linear rate, and clarified the relationships among previously scattered weakly convex optimization methods. The paper has been cited more than 2,000 times and has entered graduate textbooks.