110 citations · 225 across the 7 of their papers we have counts for
8 papers
Extragradient Method in Optimization: Convergence and Complexity
Trong Phong Nguyen, Edouard Pauwels, Emile Richard +1
We consider the extragradient method to minimize the sum of two functions, the first one being smooth and the second being convex. Under the Kurdyka-Lojasiewicz assumption, we prov…
A statistical model for tensor PCA
Andrea Montanari, Emile Richard
We consider the Principal Component Analysis problem for large tensors of arbitrary order under a single-spike (or rank-one plus noise) model. On the one hand, we use informati…
Tight convex relaxations for sparse matrix factorization
Emile Richard, Guillaume Obozinski, Jean-Philippe Vert
Based on a new atomic norm, we propose a new convex formulation for sparse matrix factorization problems in which the number of nonzero elements of the factors is assumed fixed and…
Non-negative Principal Component Analysis: Message Passing Algorithms and Sharp Asymptotics
Andrea Montanari, Emile Richard
Principal component analysis (PCA) aims at estimating the direction of maximal variability of a high-dimensional dataset. A natural question is: does this task become easier, and e…
Link Prediction in Graphs with Autoregressive Features
Emile Richard, Stephane Gaiffas, Nicolas Vayatis
In the paper, we consider the problem of link prediction in time-evolving graphs. We assume that certain graph features, such as the node degree, follow a vector autoregressive (VA…
Estimation of Simultaneously Sparse and Low Rank Matrices
Emile Richard, Pierre-Andre Savalle, Nicolas Vayatis
The paper introduces a penalized matrix estimation procedure aiming at solutions which are sparse and low-rank at the same time. Such structures arise in the context of social netw…