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Adam under Generalized Smoothness with Second-Moment-Type Stochastic Gradients
Adam converges with high probability on generalized-smooth objectives under only second-moment stochastic gradients, matching a sharp δ^{-1/2} confidence dependence and yielding expectation rates for p<1.
Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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AI panel: 11 of 20 reviewers recommend it
lenient 2/5
medium 6/10
strict 3/5