
Tight convex relaxations for sparse matrix factorization
Based on a new atomic norm, we propose a new convex formulation for spar...
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Convex Relaxation for Combinatorial Penalties
In this paper, we propose an unifying view of several recently proposed ...
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On the Equivalence between Herding and Conditional Gradient Algorithms
We show that the herding procedure of Welling (2009) takes exactly the f...
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Group Lasso with Overlaps: the Latent Group Lasso approach
We study a norm for structured sparsity which leads to sparse linear pre...
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Structured sparsity through convex optimization
Sparse estimation methods are aimed at using or obtaining parsimonious r...
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Trace Lasso: a trace norm regularization for correlated designs
Using the ℓ_1norm to regularize the estimation of the parameter vector ...
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Optimization with SparsityInducing Penalties
Sparse estimation methods are aimed at using or obtaining parsimonious r...
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Multiscale Mining of fMRI data with Hierarchical Structured Sparsity
Inverse inference, or "brain reading", is a recent paradigm for analyzin...
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Convex and Network Flow Optimization for Structured Sparsity
We consider a class of learning problems regularized by a structured spa...
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Proximal Methods for Hierarchical Sparse Coding
Sparse coding consists in representing signals as sparse linear combinat...
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Network Flow Algorithms for Structured Sparsity
We consider a class of learning problems that involve a structured spars...
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Structured Sparse Principal Component Analysis
We present an extension of sparse PCA, or sparse dictionary learning, wh...
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Support union recovery in highdimensional multivariate regression
In multivariate regression, a Kdimensional response vector is regressed...
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Canonical Tensor Decomposition for Knowledge Base Completion
The problem of Knowledge Base Completion can be framed as a 3rdorder bi...
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Learning the effect of latent variables in Gaussian Graphical models with unobserved variables
The edge structure of the graph defining an undirected graphical model d...
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A direct approach to detection and attribution of climate change
We present here a novel statistical learning approach for detection and ...
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