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Ranking and empirical minimization of U-statistics
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 rigorousExpand
Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration
TLDR
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
Selective review of offline change point detection methods
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 toExpand
On the Bayes-risk consistency of regularized boosting methods
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 certainExpand
On the Rate of Convergence of Regularized Boosting Classifiers
TLDR
A regularized boosting method is introduced, for which regularization is obtained through a penalization function. Expand
Estimation of Simultaneously Sparse and Low Rank Matrices
TLDR
The paper introduces a penalized matrix estimation procedure aiming at solutions which are sparse and low-rank at the same time. Expand
Global optimization of Lipschitz functions
TLDR
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
Ranking and Scoring Using Empirical Risk Minimization
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 ofExpand
Recursive Aggregation of Estimators by the Mirror Descent Algorithm with Averaging
TLDR
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
Tree-Based Ranking Methods
TLDR
This paper investigates how recursive partitioning methods can be adapted to the bipartite ranking problem. Expand
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