Abstract: We propose and analyze a generalized framework for distributed, delayed, and approximate stochastic gradient descent. Our framework considers n local agents who utilize their local data and ...
Abstract: Gradient Descent Ascent (GDA) methods for min-max optimization problems typically produce oscillatory behavior that can lead to instability, e.g., in bilinear settings. To address this ...
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