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Data splitting improves statistical performance in overparametrized regimes
While large training datasets generally offer improvement in model performance, the training process becomes computationally expensive and time consuming.
Stochastic Gradient Descent in Hilbert Scales: Smoothness, Preconditioning and Earlier Stopping
Stochastic Gradient Descent (SGD) has become the method of choice for solving a broad range of machine learning problems.
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