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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…
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…
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…
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…
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…
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…