14 citations · 36 across the 15 of their papers we have counts for
5 papers · 1 filter
Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolev
Xiao Wang, Qi Lei, Ioannis Panageas
Sampling is a fundamental and arguably very important task with numerous applications in Machine Learning. One approach to sample from a high dimensional distribution for…
Efficient Statistics for Sparse Graphical Models from Truncated Samples
Arnab Bhattacharyya, Rathin Desai, Sai Ganesh Nagarajan +1
In this paper, we study high-dimensional estimation from truncated samples. We focus on two fundamental and classical problems: (i) inference of sparse Gaussian graphical models an…
Convergence to Second-Order Stationarity for Non-negative Matrix Factorization: Provably and Concurrently
Ioannis Panageas, Stratis Skoulakis, Antonios Varvitsiotis +1
Non-negative matrix factorization (NMF) is a fundamental non-convex optimization problem with numerous applications in Machine Learning (music analysis, document clustering, speech…
Better Depth-Width Trade-offs for Neural Networks through the lens of Dynamical Systems
Vaggos Chatziafratis, Sai Ganesh Nagarajan, Ioannis Panageas
The expressivity of neural networks as a function of their depth, width and type of activation units has been an important question in deep learning theory. Recently, depth separat…
Last iterate convergence in no-regret learning: constrained min-max optimization for convex-concave landscapes
Qi Lei, Sai Ganesh Nagarajan, Ioannis Panageas +1
In a recent series of papers it has been established that variants of Gradient Descent/Ascent and Mirror Descent exhibit last iterate convergence in convex-concave zero-sum games.…