Recent activities

Recent interests

Ranking and interdependent data

I’m particularly interested in the problems that go beyong the classical assumption of IID training data. In particular, I’m interested in

  • theoretical aspects (e.g. generalization bounds, concentration inequalities) of learning with interdependent data;
  • bipartite ranking.

Dealing with noisy/incomplete labels

Another side of my research is devoted to learning with noisy data. I’ve worked on questions like

  • learning with classification noise, with a special emphasis on (kernel) hyperplane learning;
  • learning with weakly supervised data (which has strong connections with learning with noisy data)

(Stochastic) convex optimization

The need for convex optimization procedures is of the utmost importance for machine learning. I’ve been working on various problems requiring convex optimization. In particular, I’ve worked on

  • learning with indefinite kernels and structured sparsity with mixed-norm regularization;
  • cutting plane algorithms for noisy data;
  • stochastic optimization (in progress).

Kernels for Chemoinformatics

Over the past years, I’ve been fortunate to collaborate with Pierre Baldi and his group at IGB, on the one hand, and Pierre Mahé and Jean-Philippe Vert, on the other hand, on the design of kernels for chemoinformatics problems. The question addressed is to be able to derive (kernel) similarities both efficient (wrt computation time) and “accurate” (i.e. that make sense given a predefined problem). These similarities are to be leveraged in classifiers such as support vector machines and boosting methods.