Modified Gauss-Newton Algorithms under Noise

Gauss-Newton methods and their stochastic version have been widely used in machine learning and signal processing. Their nonsmooth counterparts, modified Gauss-Newton or prox-linear algorithms, can lead to contrasting outcomes when compared to gradient descent in large-scale statistical settings. We...

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Vydané v:	IEEE Statistical Signal Processing Workshop s. 51 - 55
Hlavní autori:	Pillutla, Krishna, Roulet, Vincent, Kakade, Sham M., Harchaoui, Zaid
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 02.07.2023
Predmet:	Composite problems Conferences Convergence Gauss-Newton Machine learning Machine learning algorithms Nonsmooth Prediction algorithms Signal processing Signal processing algorithms
ISSN:	2693-3551
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