no code implementations • 23 Oct 2023 • Anass Aghbalou, François Portier, Anne Sabourin

When dealing with imbalanced classification data, reweighting the loss function is a standard procedure allowing to equilibrate between the true positive and true negative rates within the risk measure.

no code implementations • 2 Aug 2023 • Stephan Clémençon, Nathan Huet, Anne Sabourin

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 $L^2[0, 1]$.

1 code implementation • 6 Mar 2023 • Nathan Huet, Stephan Clémençon, Anne Sabourin

The statistical learning problem consists in building a predictive function $\hat{f}$ based on independent copies of $(X, Y)$ so that $Y$ is approximated by $\hat{f}(X)$ with minimum (squared) error.

no code implementations • 7 Apr 2021 • Stéphan Clémençon, Hamid Jalalzai, Stéphane Lhaut, Anne Sabourin, Johan Segers

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.

no code implementations • NeurIPS 2020 • Hamid Jalalzai, Pierre Colombo, Chloé Clavel, Eric Gaussier, Giovanna Varni, Emmanuel Vignon, Anne Sabourin

The dominant approaches to text representation in natural language rely on learning embeddings on massive corpora which have convenient properties such as compositionality and distance preservation.

Ranked #3 on Sentiment Analysis on Yelp Binary classification

no code implementations • 25 Sep 2019 • Hamid Jalalzai, Pierre Colombo, Chloé Clavel, Eric Gaussier, Giovanna Varni, Emmanuel Vignon, Anne Sabourin

The dominant approaches to sentence representation in natural language rely on learning embeddings on massive corpuses.

1 code implementation • 17 Jul 2019 • Maël Chiapino, Stéphan Clémençon, Vincent Feuillard, Anne Sabourin

In a wide variety of situations, anomalies in the behaviour of a complex system, whose health is monitored through the observation of a random vector X = (X1,.

no code implementations • 26 Jun 2019 • Holger Drees, Anne Sabourin

Within the statistical learning framework of empirical risk minimization, our main focus is to analyze the squared reconstruction error for the exceedances over large radial thresholds.

no code implementations • NeurIPS 2018 • Hamid Jalalzai, Stephan Clémençon, Anne Sabourin

In pattern recognition, a random label Y is to be predicted based upon observing a random vector X valued in $\mathbb{R}^d$ with d>1 by means of a classification rule with minimum probability of error.

no code implementations • 27 Jul 2017 • Mastane Achab, Stephan Clémençon, Aurélien Garivier, Anne Sabourin, Claire Vernade

This paper is devoted to the study of the max K-armed bandit problem, which consists in sequentially allocating resources in order to detect extreme values.

no code implementations • 31 Mar 2016 • Nicolas Goix, Anne Sabourin, Stéphan Clémençon

Extremes play a special role in Anomaly Detection.

no code implementations • 21 Jul 2015 • Nicolas Goix, Anne Sabourin, Stéphan Clémençon

Capturing the dependence structure of multivariate extreme events is a major concern in many fields involving the management of risks stemming from multiple sources, e. g. portfolio monitoring, insurance, environmental risk management and anomaly detection.

no code implementations • 5 Feb 2015 • Nicolas Goix, Anne Sabourin, Stéphan Clémençon

Extensions to the multivariate setting are far from straightforward and it is precisely the main purpose of this paper to introduce a novel and convenient (functional) criterion for measuring the performance of a scoring function regarding the anomaly ranking task, referred to as the Excess-Mass curve (EM curve).

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.