37 citations · 45 across the 5 of their papers we have counts for
5 papers
Achieving Directional-Stationarity from a Single Random Direction Step
Dan Greenstein, Nadav Hallak
This paper addresses the challenge of obtaining strong optimality guarantees in constrained nonsmooth nonconvex optimization under mild regularity conditions, namely local Lipschit…
An Augmented Lagrangian Approach to Composite Problems with a Random Linear Operator
Dan Greenstein, Nadav Hallak
We consider the minimization of a sum of a smooth function with a nonsmooth composite function, where the composition is applied on a random linear mapping. This random composite m…
Regret minimization in stochastic non-convex learning via a proximal-gradient approach
Nadav Hallak, Panayotis Mertikopoulos, Volkan Cevher
Motivated by applications in machine learning and operations research, we study regret minimization with stochastic first-order oracle feedback in online constrained, and possibly…
Efficient Proximal Mapping of the 1-path-norm of Shallow Networks
Fabian Latorre, Paul Rolland, Nadav Hallak +1
We demonstrate two new important properties of the 1-path-norm of shallow neural networks. First, despite its non-smoothness and non-convexity it allows a closed form proximal oper…
On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems
Panayotis Mertikopoulos, Nadav Hallak, Ali Kavis +1
This paper analyzes the trajectories of stochastic gradient descent (SGD) to help understand the algorithm's convergence properties in non-convex problems. We first show that the s…