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View article: Asynchronous Gossip Algorithms for Rank-Based Statistical Methods
Asynchronous Gossip Algorithms for Rank-Based Statistical Methods Open
As decentralized AI and edge intelligence become increasingly prevalent, ensuring robustness and trustworthiness in such distributed settings has become a critical issue-especially in the presence of corrupted or adversarial data. Traditio…
View article: Robust Distributed Estimation: Extending Gossip Algorithms to Ranking and Trimmed Means
Robust Distributed Estimation: Extending Gossip Algorithms to Ranking and Trimmed Means Open
This paper addresses the problem of robust estimation in gossip algorithms over arbitrary communication graphs. Gossip algorithms are fully decentralized, relying only on local neighbor-to-neighbor communication, making them well-suited fo…
View article: Polar Coordinate-Based 2D Pose Prior with Neural Distance Field
Polar Coordinate-Based 2D Pose Prior with Neural Distance Field Open
Human pose capture is essential for sports analysis, enabling precise evaluation of athletes' movements. While deep learning-based human pose estimation (HPE) models from RGB videos have achieved impressive performance on public datasets, …
View article: Active Bipartite Ranking with Smooth Posterior Distributions
Active Bipartite Ranking with Smooth Posterior Distributions Open
International audience
Mitigating Bias in Facial Recognition Systems: Centroid Fairness Loss Optimization Open
The urging societal demand for fair AI systems has put pressure on the research community to develop predictive models that are not only globally accurate but also meet new fairness criteria, reflecting the lack of disparate mistreatment w…
View article: Weak Signals and Heavy Tails: Machine-learning meets Extreme Value Theory
Weak Signals and Heavy Tails: Machine-learning meets Extreme Value Theory Open
The masses of data now available have opened up the prospect of discovering weak signals using machine-learning algorithms, with a view to predictive or interpretation tasks. As this survey of recent results attempts to show, bringing mult…
On regression in extreme regions Open
International audience
A bipartite ranking approach to the two-sample problem Open
International audience
View article: Kernel‐Based Bootstrap Synthetic Data to Estimate Measurement Uncertainty in Analytical Sciences
Kernel‐Based Bootstrap Synthetic Data to Estimate Measurement Uncertainty in Analytical Sciences Open
Measurement uncertainty (MU) is becoming a key figure of merit for analytical methods, and estimating MU from method validation data is cost‐effective and practical. Since MU can be defined as a coverage interval of a given result, the com…
View article: Learning to rank anomalies: scalar performance criteria and maximization of rank statistics
Learning to rank anomalies: scalar performance criteria and maximization of rank statistics Open
The ability to collect and store ever more massive data, unlabeled in many cases, has been accompanied by the need to process them efficiently in order to extract relevant information and possibly design solutions based on the latter. In v…
View article: Weibull mixture estimation based on censored data with applications to clustering in reliability engineering
Weibull mixture estimation based on censored data with applications to clustering in reliability engineering Open
It is the purpose of this paper to propose a novel clustering technique tailored to randomly censored data in reliability/survival analysis. It is based on an underlying mixture model of Weibull distributions and consists in estimating its…
View article: Tail Index Estimation for Discrete Heavy-Tailed Distributions
Tail Index Estimation for Discrete Heavy-Tailed Distributions Open
It is the purpose of this paper to investigate the issue of estimating the regularity index $β>0$ of a discrete heavy-tailed r.v. $S$, \textit{i.e.} a r.v. $S$ valued in $\mathbb{N}^*$ such that $\mathbb{P}(S>n)=L(n)\cdot n^{-β}$ for all $…
View article: Flexible Parametric Inference for Space-Time Hawkes Processes
Flexible Parametric Inference for Space-Time Hawkes Processes Open
Many modern spatio-temporal data sets, in sociology, epidemiology or seismology, for example, exhibit self-exciting characteristics, triggering and clustering behaviors both at the same time, that a suitable Hawkes space-time process can a…
View article: On Ranking-based Tests of Independence
On Ranking-based Tests of Independence Open
In this paper we develop a novel nonparametric framework to test the independence of two random variables $\mathbf{X}$ and $\mathbf{Y}$ with unknown respective marginals $H(dx)$ and $G(dy)$ and joint distribution $F(dx dy)$, based on {\it …
View article: Towards More Robust NLP System Evaluation: Handling Missing Scores in Benchmarks
Towards More Robust NLP System Evaluation: Handling Missing Scores in Benchmarks Open
International audience
View article: Fighting selection bias in statistical learning: application to visual recognition from biased image databases
Fighting selection bias in statistical learning: application to visual recognition from biased image databases Open
In practice, and especially when training deep neural networks, visual recognition rules are often learned based on various sources of information. On the other hand, the recent deployment of facial recognition systems with uneven performa…
View article: Concentration bounds for the empirical angular measure with statistical learning applications
Concentration bounds for the empirical angular measure with statistical learning applications Open
The angular measure on the unit sphere characterizes the first-order dependence structure of the components of a random vector in extreme regions and is defined in terms of standardized margins. Its statistical recovery is an important ste…
View article: Regular Variation in Hilbert Spaces and Principal Component Analysis for Functional Extremes
Regular Variation in Hilbert Spaces and Principal Component Analysis for Functional Extremes Open
Motivated by the increasing availability of data of functional nature, we develop a general probabilistic and statistical framework for extremes of regularly varying random elements $X$ in $L^2[0,1]$. We place ourselves in a Peaks-Over-Thr…
View article: On Regression in Extreme Regions
On Regression in Extreme Regions Open
We establish a statistical learning theoretical framework aimed at extrapolation, or out-of-domain generalization, on the unobserved tails of covariates in continuous regression problems. Our strategy involves performing statistical regres…
View article: A Bipartite Ranking Approach to the Two-Sample Problem
A Bipartite Ranking Approach to the Two-Sample Problem Open
The two-sample problem, which consists in testing whether independent samples on $\mathbb{R}^d$ are drawn from the same (unknown) distribution, finds applications in many areas. Its study in high-dimension is the subject of much attention,…
View article: Reconstruction of Trajectories of Athletes Using Computer Vision Models and Kinetic Analysis
Reconstruction of Trajectories of Athletes Using Computer Vision Models and Kinetic Analysis Open
International audience
View article: Affine invariant integrated rank-weighted statistical depth: properties and finite sample analysis
Affine invariant integrated rank-weighted statistical depth: properties and finite sample analysis Open
International audience
View article: Universal aggregation of permutations
Universal aggregation of permutations Open
International audience
View article: Assessing Uncertainty in Similarity Scoring: Performance & Fairness in Face Recognition
Assessing Uncertainty in Similarity Scoring: Performance & Fairness in Face Recognition Open
The ROC curve is the major tool for assessing not only the performance but also the fairness properties of a similarity scoring function. In order to draw reliable conclusions based on empirical ROC analysis, accurately evaluating the unce…
View article: On Medians of (Randomized) Pairwise Means
On Medians of (Randomized) Pairwise Means Open
Tournament procedures, recently introduced in Lugosi & Mendelson (2016), offer an appealing alternative, from a theoretical perspective at least, to the principle of Empirical Risk Minimization in machine learning. Statistical learning by …
View article: Mitigating Gender Bias in Face Recognition Using the von Mises-Fisher Mixture Model
Mitigating Gender Bias in Face Recognition Using the von Mises-Fisher Mixture Model Open
In spite of the high performance and reliability of deep learning algorithms in a wide range of everyday applications, many investigations tend to show that a lot of models exhibit biases, discriminating against specific subgroups of the p…