
Learning with SemiDefinite Programming: new statistical bounds based on fixed point analysis and excess risk curvature
Many statistical learning problems have recently been shown to be amenab...
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Boolean learning under noiseperturbations in hardware neural networks
A high efficiency hardware integration of neural networks benefits from ...
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Revisiting clustering as matrix factorisation on the Stiefel manifold
This paper studies clustering for possibly high dimensional data (e.g. i...
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Multikernel unmixing and superresolution using the Modified Matrix Pencil method
Consider L groups of point sources or spike trains, with the l^th group ...
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Online shortest paths with confidence intervals for routing in a time varying random network
The increase in the world's population and rising standards of living is...
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Hedging parameter selection for basis pursuit
In Compressed Sensing and high dimensional estimation, signal recovery o...
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Feature selection in weakly coherent matrices
A problem of paramount importance in both pure (Restricted Invertibility...
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Average performance analysis of the stochastic gradient method for online PCA
This paper studies the complexity of the stochastic gradient algorithm f...
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PostPrognostics Decision for Optimizing the Commitment of Fuel Cell Systems
In a postprognostics decision context, this paper addresses the problem...
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Finding optimal finite biological sequences over finite alphabets: the OptiFin toolbox
In this paper, we present a toolbox for a specific optimization problem ...
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Small coherence implies the weak Null Space Property
In the Compressed Sensing community, it is well known that given a matri...
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A SemiDefinite Programming approach to low dimensional embedding for unsupervised clustering
This paper proposes a variant of the method of Guédon and Verhynin for e...
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Convex recovery of tensors using nuclear norm penalization
The subdifferential of convex functions of the singular spectrum of real...
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Mixture model for designs in high dimensional regression and the LASSO
The LASSO is a recent technique for variable selection in the regression...
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Sparse recovery with unknown variance: a LASSOtype approach
We address the issue of estimating the regression vector β in the generi...
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Stéphane Chrétien
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