We consider the least-square regression problem with regularization by a block 1-norm, i.e., a sum of Euclidean norms over spaces of dimensions large…

In the framework of finite games in extensive form with perfect information and strict preferences, this paper introduces a new equilibrium concept: …

In multi-task learning several related tasks are considered simultaneously, with the hope that by an appropriate sharing of information across tasks,…

We propose a convex-concave programming approach for the labeled weighted graph matching problem. The convex-concave programming formulation is obtai…

Disclinations, first observed in mesomorphic phases, are relevant to a number of ill-ordered condensed matter media, with continuous symmetries or fr…

For supervised and unsupervised learning, positive definite kernels allow to use large and potentially infinite dimensional feature spaces with a com…

Aligning protein-protein interaction (PPI) networks of different species has drawn a considerable interest recently. This problem is important to inv…

We study a norm for structured sparsity which leads to sparse linear predictors whose supports are unions of prede ned overlapping groups of variable…

We present a general approach for collaborative filtering (CF) using spectral regularization to learn linear operators from "users" to the "objects" …

The theory of defects in ordered and ill-ordered media is a well-advanced part of condensed matter physics. Concepts developed in this field also occ…

We consider the least-square linear regression problem with regularization by the l1-norm, a problem usually referred to as the Lasso. In this paper,…

We consider the least-square linear regression problem with regularization by the $\ell^1$-norm, a problem usually referred to as the Lasso. In this …

Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a linear combination of the input variables while co…

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