362 citations · 2.5k across the 76 of their papers we have counts for
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Conditional Gradient Method for Stochastic Submodular Maximization: Closing the Gap
Aryan Mokhtari, Hamed Hassani, Amin Karbasi
In this paper, we study the problem of \textit{constrained} and \textit{stochastic} continuous submodular maximization. Even though the objective function is not concave (nor conve…
First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization
Aryan Mokhtari, Alejandro Ribeiro
This paper studies empirical risk minimization (ERM) problems for large-scale datasets and incorporates the idea of adaptive sample size methods to improve the guaranteed convergen…
A Newton-Based Method for Nonconvex Optimization with Fast Evasion of Saddle Points
Santiago Paternain, Aryan Mokhtari, Alejandro Ribeiro
Machine learning problems such as neural network training, tensor decomposition, and matrix factorization, require local minimization of a nonconvex function. This local minimizati…
Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method
Mark Eisen, Aryan Mokhtari, Alejandro Ribeiro
We consider large scale empirical risk minimization (ERM) problems, where both the problem dimension and variable size is large. In these cases, most second order methods are infea…
IQN: An Incremental Quasi-Newton Method with Local Superlinear Convergence Rate
Aryan Mokhtari, Mark Eisen, Alejandro Ribeiro
The problem of minimizing an objective that can be written as the sum of a set of smooth and strongly convex functions is considered. The Incremental Quasi-Newton (IQN) method…