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stat.ML2014★ 23 cited
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…
stat.ML2012
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…