Roberto D'Ambrosio

Learn More
It is an admitted fact that mainstream boosting algorithms like AdaBoost do not perform well to estimate class conditional probabilities. In this paper, we analyze, in the light of this problem, a recent algorithm , unn, which leverages nearest neighbors while minimizing a convex loss. Our contribution is threefold. First, we show that there exists a(More)
Research in automatic facial expression recognition has permitted the development of systems discriminating between the six pro-totypical expressions, i.e. anger, disgust, fear, happiness, sadness and surprise, in frontal video sequences. Achieving high recognition rate often implies high computational costs that are not compatible with real time(More)
Tailoring nearest neighbors algorithms to boosting is an important problem. Recent papers study an approach, UNN, which provably minimizes particular convex surrogates under weak assumptions. However, numerical issues make it necessary to experimentally tweak parts of the UNN algorithm, at the possible expense of the algorithm's convergence and performance.(More)
This paper presents an original approach for jointly fitting survival times and classifying samples into subgroups. The Coxlogit model is a generalized linear model with a common set of selected features for both tasks. Survival times and class labels are here assumed to be conditioned by a common risk score which depends on those features. Learning is then(More)