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20052025
most citedSelective review of offline change point detection methods

1.4k citations · 1.9k across the 49 of their papers we have counts for

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6 papers · 1 filter

math.ST2023

A Bipartite Ranking Approach to the Two-Sample Problem

Stephan Clémençon, Myrto Limnios, Nicolas Vayatis

The two-sample problem, which consists in testing whether independent samples on are drawn from the same (unknown) distribution, finds applications in many areas. It…

math.ST2021★ 4 cited

Concentration Inequalities for Two-Sample Rank Processes with Application to Bipartite Ranking

Stéphan Clémençon, Myrto Limnios, Nicolas Vayatis

The ROC curve is the gold standard for measuring the performance of a test/scoring statistic regarding its capacity to discriminate between two statistical populations in a wide va…

math.ST2013★ 1 cited

Sloshing in the LNG shipping industry: risk modelling through multivariate heavy-tail analysis

Antoine Dematteo, Stéphan CLEMENCON, Nicolas Vayatis +1

In the liquefied natural gas (LNG) shipping industry, the phenomenon of sloshing can lead to the occurrence of very high pressures in the tanks of the vessel. The issue of modellin…

math.ST2006★ 80 cited

Ranking the best instances

Stéphan Clémençon, Nicolas Vayatis

We formulate the local ranking problem in the framework of bipartite ranking where the goal is to focus on the best instances. We propose a methodology based on the construction of…

math.ST2006

Ranking and empirical minimization of U-statistics

Stéphan Clémençon, Gábor Lugosi, Nicolas Vayatis

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 proble…

math.ST2005★ 1 cited

Recursive Aggregation of Estimators by Mirror Descent Algorithm with Averaging

Anatoli Juditsky, Alexander Nazin, Alexandre Tsybakov +1

We consider a recursive algorithm to construct an aggregated estimator from a finite number of base decision rules in the classification problem. The estimator approximately minimi…