77 citations · 110 across the 5 of their papers we have counts for
5 papers
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…
Graph Prediction in a Low-Rank and Autoregressive Setting
Emile Richard, Pierre-Andre Savalle, Nicolas Vayatis
We study the problem of prediction for evolving graph data. We formulate the problem as the minimization of a convex objective encouraging sparsity and low-rank of the solution, th…
A Regularization Approach for Prediction of Edges and Node Features in Dynamic Graphs
Emile Richard, Andreas Argyriou, Theodoros Evgeniou +1
We consider the two problems of predicting links in a dynamic graph sequence and predicting functions defined at each node of the graph. In many applications, the solution of one p…