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cs.LG2024
Exploring the loss landscape of regularized neural networks via convex duality
Sungyoon Kim, Aaron Mishkin, Mert Pilanci
We discuss several aspects of the loss landscape of regularized neural networks: the structure of stationary points, connectivity of optimal solutions, path with nonincreasing loss…
math.OC2024
Faster Convergence of Stochastic Accelerated Gradient Descent under Interpolation
Aaron Mishkin, Mert Pilanci, Mark Schmidt
We prove new convergence rates for a generalized version of stochastic Nesterov acceleration under interpolation conditions. Unlike previous analyses, our approach accelerates any…
cs.LG2024
A Library of Mirrors: Deep Neural Nets in Low Dimensions are Convex Lasso Models with Reflection Features
Emi Zeger, Yifei Wang, Aaron Mishkin +3
We prove that training neural networks on 1-D data is equivalent to solving convex Lasso problems with discrete, explicitly defined dictionary matrices. We consider neural networks…