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20062009
most citedClustered Multi-Task Learning: A Convex Formulation

285 citations · 746 across the 7 of their papers we have counts for

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cs.LG2009

Self-concordant analysis for logistic regression

Francis Bach

Most of the non-asymptotic theoretical work in regression is carried out for the square loss, where estimators can be obtained through closed-form expressions. In this paper, we us…

cs.LG2008285 cited

Clustered Multi-Task Learning: A Convex Formulation

Laurent Jacob, Francis Bach, Jean-Philippe Vert

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

cs.LG2008176 cited

Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning

Francis Bach

For supervised and unsupervised learning, positive definite kernels allow to use large and potentially infinite dimensional feature spaces with a computational cost that only depen…

cs.LG2007

Graph kernels between point clouds

Francis Bach

Point clouds are sets of points in two or three dimensions. Most kernel methods for learning on sets of points have not yet dealt with the specific geometrical invariances and prac…

cs.LG2007183 cited

Consistency of trace norm minimization

Francis Bach

Regularization by the sum of singular values, also referred to as the trace norm, is a popular technique for estimating low rank rectangular matrices. In this paper, we extend some…

cs.LG2006100 cited

Low-rank matrix factorization with attributes

Jacob Abernethy, Francis Bach, Theodoros Evgeniou +1

We develop a new collaborative filtering (CF) method that combines both previously known users' preferences, i.e. standard CF, as well as product/user attributes, i.e. classical fu…