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On Regularization via Early Stopping for Least Squares Regression
Rishi Sonthalia, Jackie Lok, Elizaveta Rebrova
A fundamental problem in machine learning is understanding the effect of early stopping on the parameters obtained and the generalization capabilities of the model. Even for linear…
Data-Driven, ML-assisted Approaches to Problem Well-Posedness
Tom Bertalan, George A. Kevrekidis, Eleni D Koronaki +3
Classically, to solve differential equation problems, it is necessary to specify sufficient initial and/or boundary conditions so as to allow the existence of a unique solution. We…
Stochastic gradient descent for streaming linear and rectified linear systems with adversarial corruptions
Halyun Jeong, Deanna Needell, Elizaveta Rebrova
We propose SGD-exp, a stochastic gradient descent approach for linear and ReLU regressions under Massart noise (adversarial semi-random corruption model) for the fully streaming se…