Stephan Clémençon

research

∙ 08/02/2023

Regular Variation in Hilbert Spaces and Principal Component Analysis for Functional Extremes

Motivated by the increasing availability of data of functional nature, w...

0 Stephan Clémençon, et al. ∙

research

∙ 05/17/2023

Towards More Robust NLP System Evaluation: Handling Missing Scores in Benchmarks

The evaluation of natural language processing (NLP) systems is crucial f...

0 Anas Himmi, et al. ∙

research

∙ 03/22/2023

Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues

As the issue of robustness in AI systems becomes vital, statistical lear...

0 Morgane Goibert, et al. ∙

research

∙ 03/06/2023

On Regression in Extreme Regions

In the classic regression problem, the value of a real-valued random var...

0 Nathan Huet, et al. ∙

research

∙ 02/07/2023

A Bipartite Ranking Approach to the Two-Sample Problem

The two-sample problem, which consists in testing whether independent sa...

0 Stephan Clémençon, et al. ∙

research

∙ 11/14/2022

Assessing Performance and Fairness Metrics in Face Recognition - Bootstrap Methods

The ROC curve is the major tool for assessing not only the performance b...

0 Jean-Rémy Conti, et al. ∙

research

∙ 11/01/2022

On Medians of (Randomized) Pairwise Means

Tournament procedures, recently introduced in Lugosi Mendelson (2016...

0 Pierre Laforgue, et al. ∙

research

∙ 10/24/2022

Mitigating Gender Bias in Face Recognition Using the von Mises-Fisher Mixture Model

In spite of the high performance and reliability of deep learning algori...

0 Jean-Rémy Conti, et al. ∙

research

∙ 02/23/2022

EMOTHAW: A novel database for emotional state recognition from handwriting

The detection of negative emotions through daily activities such as hand...

0 Laurence Likforman-Sulem, et al. ∙

research

∙ 02/15/2022

A Statistical Learning View of Simple Kriging

In the Big Data era, with the ubiquity of geolocation sensors in particu...

1 Emilia Siviero, et al. ∙

research

∙ 02/08/2022

What are the best systems? New perspectives on NLP Benchmarking

In Machine Learning, a benchmark refers to an ensemble of datasets assoc...

12 Pierre Colombo, et al. ∙

research

∙ 01/20/2022

Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications

The concept of median/consensus has been widely investigated in order to...

0 Morgane Goibert, et al. ∙

research

∙ 01/17/2022

Improving the quality control of seismic data through active learning

In image denoising problems, the increasing density of available images ...

0 Mathieu Chambefort, et al. ∙

research

∙ 01/13/2022

Functional Anomaly Detection: a Benchmark Study

The increasing automation in many areas of the Industry expressly demand...

15 Guillaume Staerman, et al. ∙

research

∙ 09/20/2021

Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Two-Sample Rank Statistics

The ability to collect and store ever more massive databases has been ac...

0 Myrto Limnios, et al. ∙

research

∙ 09/06/2021

Visual Recognition with Deep Learning from Biased Image Datasets

In practice, and more especially when training deep neural networks, vis...

10 Robin Vogel, et al. ∙

research

∙ 07/27/2021

Individual Survival Curves with Conditional Normalizing Flows

Survival analysis, or time-to-event modelling, is a classical statistica...

0 Guillaume Ausset, et al. ∙

research

∙ 06/21/2021

Affine-Invariant Integrated Rank-Weighted Depth: Definition, Properties and Finite Sample Analysis

Because it determines a center-outward ordering of observations in ℝ^d w...

0 Guillaume Staerman, et al. ∙

research

∙ 04/07/2021

Concentration bounds for the empirical angular measure with statistical learning applications

The angular measure on the unit sphere characterizes the first-order dep...

0 Stephan Clémençon, et al. ∙

research

∙ 04/07/2021

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

The ROC curve is the gold standard for measuring the performance of a te...

0 Stephan Clémençon, et al. ∙

research

∙ 03/29/2021

Dynamically Modelling Heterogeneous Higher-Order Interactions for Malicious Behavior Detection in Event Logs

Anomaly detection in event logs is a promising approach for intrusion de...

0 Corentin Larroche, et al. ∙

research

∙ 03/23/2021

Depth-based pseudo-metrics between probability distributions

Data depth is a non parametric statistical tool that measures centrality...

0 Guillaume Staerman, et al. ∙

research

∙ 06/26/2020

Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications

Motivated by a wide variety of applications, ranging from stochastic opt...

0 Guillaume Ausset, et al. ∙

research

∙ 06/09/2020

How Robust is the Median-of-Means? Concentration Bounds in Presence of Outliers

In contrast to the empirical mean, the Median-of-Means (MoM) is an estim...

0 Pierre Laforgue, et al. ∙

research

∙ 03/13/2020

Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach

The recent enthusiasm for artificial intelligence (AI) is due principall...

0 Valérie Beaudouin, et al. ∙

research

∙ 02/21/2020

A Multiclass Classification Approach to Label Ranking

In multiclass classification, the goal is to learn how to predict a rand...

0 Stephan Clémençon, et al. ∙

research

∙ 02/19/2020

Learning Fair Scoring Functions: Fairness Definitions, Algorithms and Generalization Bounds for Bipartite Ranking

Many applications of artificial intelligence, ranging from credit lendin...

0 Robin Vogel, et al. ∙

research

∙ 02/12/2020

Weighted Empirical Risk Minimization: Sample Selection Bias Correction based on Importance Sampling

We consider statistical learning problems, when the distribution P' of t...

0 Robin Vogel, et al. ∙

research

∙ 10/09/2019

The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth Measure

With the ubiquity of sensors in the IoT era, statistical observations ar...

0 Guillaume Staerman, et al. ∙

research

∙ 07/17/2019

A Multivariate Extreme Value Theory Approach to Anomaly Clustering and Visualization

In a wide variety of situations, anomalies in the behaviour of a complex...

0 Maël Chiapino, et al. ∙

research

∙ 06/28/2019

Statistical Learning from Biased Training Samples

With the deluge of digitized information in the Big Data era, massive da...

0 Pierre Laforgue, et al. ∙

research

∙ 06/21/2019

On Tree-based Methods for Similarity Learning

In many situations, the choice of an adequate similarity measure or metr...

0 Stephan Clémençon, et al. ∙

research

∙ 06/21/2019

Trade-offs in Large-Scale Distributed Tuplewise Estimation and Learning

The development of cluster computing frameworks has allowed practitioner...

2 Robin Vogel, et al. ∙

research

∙ 06/05/2019

Empirical Risk Minimization under Random Censorship: Theory and Practice

We consider the classic supervised learning problem, where a continuous ...

0 Guillaume Ausset, et al. ∙

research

∙ 04/09/2019

Functional Isolation Forest

For the purpose of monitoring the behavior of complex infrastructures (e...

0 Guillaume Staerman, et al. ∙

research

∙ 10/15/2018

Dimensionality Reduction and (Bucket) Ranking: a Mass Transportation Approach

Whereas most dimensionality reduction techniques (e.g. PCA, ICA, NMF) fo...

0 Mastane Achab, et al. ∙

research

∙ 07/18/2018

A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization

The performance of many machine learning techniques depends on the choic...

0 Robin Vogel, et al. ∙

research

∙ 05/28/2018

Autoencoding any Data through Kernel Autoencoders

This paper investigates a novel algorithmic approach to data representat...

0 Pierre Laforgue, et al. ∙

research

∙ 05/08/2018

Profitable Bandits

Originally motivated by default risk management applications, this paper...

0 Mastane Achab, et al. ∙

research

∙ 01/17/2018

Ranking Data with Continuous Labels through Oriented Recursive Partitions

We formulate a supervised learning problem, referred to as continuous ra...

0 Stephan Clémençon, et al. ∙

research

∙ 10/31/2017

Ranking Median Regression: Learning to Order through Local Consensus

This article is devoted to the problem of predicting the value taken by ...

0 Stephan Clémençon, et al. ∙

research

∙ 07/27/2017

Max K-armed bandit: On the ExtremeHunter algorithm and beyond

This paper is devoted to the study of the max K-armed bandit problem, wh...

0 Mastane Achab, et al. ∙

research

∙ 05/03/2017

Mass Volume Curves and Anomaly Ranking

This paper aims at formulating the issue of ranking multivariate unlabel...

0 Stephan Clémençon, et al. ∙

research

∙ 06/08/2016

Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions

In decentralized networks (of sensors, connected objects, etc.), there i...

0 Igor Colin, et al. ∙

research

∙ 03/31/2016

Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking

Extremes play a special role in Anomaly Detection. Beyond inference and ...

0 Nicolas Goix, et al. ∙

research

∙ 11/17/2015

Extending Gossip Algorithms to Distributed Estimation of U-Statistics

Efficient and robust algorithms for decentralized estimation in networks...

0 Igor Colin, et al. ∙

research

∙ 08/25/2015

AUC Optimisation and Collaborative Filtering

In recommendation systems, one is interested in the ranking of the predi...

0 Charanpal Dhanjal, et al. ∙

research

∙ 02/05/2015

On Anomaly Ranking and Excess-Mass Curves

Learning how to rank multivariate unlabeled observations depending on th...

0 Nicolas Goix, et al. ∙

research

∙ 01/12/2015

Scaling-up Empirical Risk Minimization: Optimization of Incomplete U-statistics

In a wide range of statistical learning problems such as ranking, cluste...

0 Stephan Clémençon, et al. ∙

research

∙ 01/09/2015

Survey schemes for stochastic gradient descent with applications to M-estimation

In certain situations that shall be undoubtedly more and more common in ...

0 Stephan Clémençon, et al. ∙

Stephan Clémençon

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