9 citations · 11 across the 3 of their papers we have counts for
8 papers
Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications
Morgane Goibert, Stéphan Clémençon, Ekhine Irurozki +1
The concept of median/consensus has been widely investigated in order to provide a statistical summary of ranking data, i.e. realizations of a random permutation of a finite se…
Functional Anomaly Detection: a Benchmark Study
Guillaume Staerman, Eric Adjakossa, Pavlo Mozharovskyi +3
The increasing automation in many areas of the Industry expressly demands to design efficient machine-learning solutions for the detection of abnormal events. With the ubiquitous d…
Approximate computation of projection depths
Rainer Dyckerhoff, Pavlo Mozharovskyi, Stanislav Nagy
Data depth is a concept in multivariate statistics that measures the centrality of a point in a given data cloud in $\IR^d$. If the depth of a point can be represented as the minim…
Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach
Valérie Beaudouin, Isabelle Bloch, David Bounie +6
The recent enthusiasm for artificial intelligence (AI) is due principally to advances in deep learning. Deep learning methods are remarkably accurate, but also opaque, which limits…
The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth Measure
Guillaume Staerman, Pavlo Mozharovskyi, Stephan Clémençon
With the ubiquity of sensors in the IoT era, statistical observations are becoming increasingly available in the form of massive (multivariate) time-series. Formulated as unsupervi…
Functional Isolation Forest
Guillaume Staerman, Pavlo Mozharovskyi, Stephan Clémençon +1
For the purpose of monitoring the behavior of complex infrastructures (e.g. aircrafts, transport or energy networks), high-rate sensors are deployed to capture multivariate data, g…