285 citations · 646 across the 5 of their papers we have counts for
6 papers
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…
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…
A path following algorithm for the graph matching problem
Mikhail Zaslavskiy, Francis Bach, Jean-Philippe Vert
We propose a convex-concave programming approach for the labeled weighted graph matching problem. The convex-concave programming formulation is obtained by rewriting the weighted g…
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…
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…
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…