I am working in the field of statistical learning/nonparametric statistics. I focus in particular on the problem of hyperparameter tuning for learning algorithms. I try to obtain methodological improvements from an in depth theoretical understanding of model selection issues. So far, I studied the socalled Slope Heuristics as well as resampling (penalization) methods. A central theoretical aspect of this line of research, based on Empirical Process Theory, is to obtain precise concentration bounds for the excess risk of Mestimators. I also work on the related subjects of learning from Markov chains, robust learning (i.e. learning with heavy tailed data), sketching for massive data, Bregman clustering, Smallball approach in Learning Theory and nonparametric statistics, as well a some probabilistic inequalities linked to logconcave measures. Email: asaumardatgmaildotcom Phone: +33 (0)2 99 05 32 10 Curriculum Vitae [pdf] Research topics
