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Strong Gaussian Approximation of Constant-Learning-Rate SGD: Bias and Trajectory Inference
This paper develops strong Gaussian approximations (KMT type) for constant learning rate stochastic gradient descent (SGD). We work with heavy tailed noise and assume that the number of iterates grows and the learning rate shrinks simultaneously. Our work constructs simple Gaussian processes on the
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