On the Double Descent of Random Features Models Trained with SGD

We study generalization properties of random features (RF) regression in high dimensions optimized by stochastic gradient descent (SGD) in under-/over-parameterized regime. In this work, we derive precise non-asymptotic error bounds of RF regression under both constant and polynomial-decay step-size...

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Hauptverfasser:	Liu, Fanghui, Suykens, Johan A. K, Cevher, Volkan
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning Statistics - Machine Learning
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