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Learning-Rate-Free Learning by D-Adaptation.

Aaron Defazio (FAIR), Konstantin Mishchenko (Samsung AI Center)

This paper introduces an interesting approach that aims to address the challenge of obtaining a learning rate free optimal bound for non-smooth stochastic convex optimization. The authors propose a novel method that overcomes the limitations imposed by traditional learning rate selection in optimizing such problems. This research makes a valuable and practical contribution to the field of optimization.