The problem of ranking/ordering instances, instead of simply classifying them, has recently gained much attention in machine learning. In this paper we formulate the ranking problem in a rigorous… Expand

We introduce a novel algorithm called GP-UCBPE based on the Gaussian process approach which combines the benefits of the UCB policy with Pure Exploration queries in the same batch of K evaluations of f .Expand

This article presents a selective survey of algorithms for the offline detection of multiple change points in multivariate time series. A general yet structuring methodological strategy is adopted to… Expand

The probability of error of classification methods based on convex combinations of simple base classifiers by boosting algorithms is investigated. The main result of the paper is that certain… Expand

The goal of the paper is to design sequential strategies which lead to efficient optimization of an unknown function under the only assumption that it has a finite Lipschitz constant.Expand

A general model is proposed for studying ranking problems. We investigate learning methods based on empirical minimization of the natural estimates of the ranking risk. The empirical estimates are of… Expand

We propose a stochastic version of the mirror descent algorithm which performs descent of the gradient type in the dual space with an additional averaging.Expand