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AdaGrad-Diff: a Corrected Version of the Adaptive Gradient Algorithm for Composite Optimization
Vanilla gradient methods are often highly sensitive to the choice of stepsize, which typically requires manual tuning. Adaptive methods alleviate this issue, with AdaGrad being a prominent example. In this work, we first show that, in composite convex optimization, AdaGrad may fail to exploit the Hö
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