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Accelerated Parameter-Free Stochastic Optimization
We propose a method that achieves near-optimal rates for smooth stochastic convex optimization and requires essentially no prior knowledge of problem parameters.
Gradient Descent Monotonically Decreases the Sharpness of Gradient Flow Solutions in Scalar Networks and Beyond
Using this result, we characterize settings where GD provably converges to the EoS in scalar networks.
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