Selected Publications

We show that the widely used Kendall tau correlation coefficient, and the related Mallows kernel, are positive definite kernels for permutations. They offer computationally attractive alternatives to more complex kernels on the symmetric group to learn from rankings, or learn to rank. We show how to extend these kernels to partial rankings, multivariate rankings and uncertain rankings. Examples are presented on how to formulate typical problems of learning from rankings such that they can be solved with state-of-the-art kernel algorithms. We demonstrate promising results on clustering heterogeneous rank data and high-dimensional classification problems in biomedical applications.

Due to its numerous applications, rank aggregation has become a problem of major interest across many fields of the computer science literature. In the vast majority of situations, Kemeny consensus(es) are considered as the ideal solutions. It is however well known that their computation is NP-hard. Many contributions have thus established various results to apprehend this complexity. In this paper we introduce a practical method to predict, for a ranking and a dataset, how close the Kemeny consensus(es) are to this ranking. A major strength of this method is its generality: it does not require any assumption on the dataset nor the ranking. Furthermore, it relies on a new geometric interpretation of Kemeny aggregation that, we believe, could lead to many other results.
ICML, 2016

Recent Publications

. Network-based Wavelet Smoothing for Analysis of Genomic Data. Under preparation, 2017.

. Signaling Pathway Activities Improve Prognosis for Breast Cancer. Submitted, 2017.

Preprint Code

. Kernel Multitask Regression for Toxicogenetics. Molecular Informatics, 2017.

Preprint Code Dataset Video HTML

. Failure State Prediction for Automated Analyzers for Analyzing a Biological Sample. Pending European Patent, 2016.

. Prediction of human population responses to toxic compounds by a collaborative competition. Nature Biotechnology, 2015.



I am (co-)author and maintainer of the following software:

  • kernrank - R package implementing kernel functions and kernel methods for analyzing rank data.

  • kmr - R implementation of a kernel multitask regression algorithm to solve simultaneously several regression problems.