226 citations · 1.9k across the 128 of their papers we have counts for
9 papers · 1 filter
Riemannian Optimization via Frank-Wolfe Methods
Melanie Weber, Suvrit Sra
We study projection-free methods for constrained Riemannian optimization. In particular, we propose the Riemannian Frank-Wolfe (RFW) method. We analyze non-asymptotic convergence r…
A Generic Approach for Escaping Saddle points
Sashank J Reddi, Manzil Zaheer, Suvrit Sra +4
A central challenge to using first-order methods for optimizing nonconvex problems is the presence of saddle points. First-order methods often get stuck at saddle points, greatly d…
Distributional Adversarial Networks
Chengtao Li, David Alvarez-Melis, Keyulu Xu +2
We propose a framework for adversarial training that relies on a sample rather than a single sample point as the fundamental unit of discrimination. Inspired by discrepancy measure…
Unsupervised robust nonparametric learning of hidden community properties
Mikhail A. Langovoy, Akhilesh Gotmare, Martin Jaggi
We consider learning of fundamental properties of communities in large noisy networks, in the prototypical situation where the nodes or users are split into two classes according t…
Global optimality conditions for deep neural networks
Chulhee Yun, Suvrit Sra, Ali Jadbabaie
We study the error landscape of deep linear and nonlinear neural networks with the squared error loss. Minimizing the loss of a deep linear neural network is a nonconvex problem, a…
An Alternative to EM for Gaussian Mixture Models: Batch and Stochastic Riemannian Optimization
Reshad Hosseini, Suvrit Sra
We consider maximum likelihood estimation for Gaussian Mixture Models (Gmms). This task is almost invariably solved (in theory and practice) via the Expectation Maximization (EM) a…