Author

Depen Morwani

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#machine learning Oct 2025

Adam or Gauss-Newton? A Comparative Study In Terms of Basis Alignment and SGD Noise

The theoretical results show that GN's optimality for linear regression no longer holds under a poorly chosen basis under both population and stochastic updates, or a move from linear regression to a non-convex variant of logistic regression, and Adam-style methods offer genuine advantages over curvature-inspired preco...

Bingbin Liu, Rachit Bansal, Depen Morwani et al. · arXiv.org · 3 citations

Jul 2026

A Defense of the Quadratic Model

Due to the complexity of neural network loss landscapes, optimization theory is forced to rely on idealized models, and there is generally a tradeoff between how theoretically tractable the model is, and how accurately it describes the true optimization dynamics. In this work, we stress test the simplest possible model...

Alexandru Meterez, Pranav Ajit Nair, Depen Morwani et al. · arXiv.org · 3 citations