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classifier. In the first step the weights of the mixture are estimated in the second step an empirical risk procedure is performed to estimate the parameters of the Hawkes processes. We establish the consistency multivariate Hawkes processes. The challenge here is the high-dimension of the classification problem which can obtained consistency in support of the LASSO the consistency of the classifier. Joint work with Christophe Denis Résumé We investigate the multiclass classification problem where the features are event sequences. More
Bootstrap GSBB . The GSBB preserves the periodic structure of the data and in result the consistent estimators other fields. Periodicity is often present not only in the mean but also in the covariance function. Thus apply the GSBB one needs to know the period length. Sometimes it may happen that period length is not known can be used. We discuss the consistency of the GSBB and the EMBB for parameters associated with PC time series these are the overall mean seasonal means the autocovariance function and the Fourier coefficients
estimation. The gap-depend rate reveals the importance of the between group gap for the difficulty of the problem instances where the problem is no more difficult than its unbiased counterpart. This is a joint work with She receives a reward corresponding to the covariates of the action that she has chosen but only observe biased evaluation of this reward where the bias depends on the sensitive attribute. We design a Fair Phased worst case up to possible log terms . The worst case regret is shown to be much larger than in classical The price of unfairness in linear bandits with biased feedback
weights if the weights are atomless the maximum weight matching is uniquely defined. We study the asymptotic object of the graph called the unimodular Bienaymé Galton Watson tree. We also show when the weights are This work is a continuation of the objective method introduced by D. Aldous in 2000 and is based on two show that the optimum matching seen as a geometric graph converges locally to a matching on the limiting are atomic the convergence of the tiebreaked maximum weight matching. This convergence generalizes to
Université Pierre et Marie Curie UPMC 4 Place Jussieu 75005 Paris Manufacture de l Encyclopédie - Geneviève Cammagre U. Toulouse Le Mirail articles BIZARRE FANTASQUE CAPRICIEUX BOURRU BISARRE Synonym. de Jaucourt - Liliane Picciola U. Université Paris Nanterre l article TRAGÉDIE Poésie les articles BIBLIOMANE BIBLIOMANIE - Sophie Lefay U. d Orléans articles de Marmontel CRITIQUE DEFINITION Leca-Tsiomis Cslf U. Université Paris Nanterre Irène Passeron Cnrs IMJ-PRG Alain Cernuschi U. de Lausanne
Université Pierre et Marie Curie UPMC 4 Place Jussieu 75005 Paris Christophe Corbier Cnrs CRAL Ehess et Malou Haine U. L. de Bruxelles articles Trétracorde et Syrinx - Leca-Tsiomis Cslf U. Université Paris Nanterre Irène Passeron Cnrs IMJ-PRG Alain Cernuschi U. de Lausanne
Triquenaux U. Lyon 2 article CONVERSATION ENTRETIEN Gramm. de D'Alembert - Frédéric Magnin Cnrs IMBE U. Aix-Marseille d'étude Manufacture de l Encyclopédie - Pierre Chartier U. Denis Diderot article INTOLÉRANCE Morale. de Diderot Leca-Tsiomis Cslf U. Université Paris Nanterre Irène Passeron Cnrs IMJ-PRG Alain Cernuschi U. de Lausanne
Algorithmic Fairness is an established area of machine learning willing to reduce the influence of hidden hidden bias in the data.Yet despite its wide range of applications very few works consider the multi-class setting from the fairness perspective.In this talk we focus on this question and extend the definition fairness in the case of Demographic Parity to multi-class classification.We specify the corresponding expressions of the optimal fair classifiers and describe a post-processing approach based on the plug-in principle
chains are introduced for the beta null recurrent case. A variation of the Central Limit Theorem for for a random number of summands is also presented.
Résumé The purpose of the conference is to gather scientists from different fields statistics time series
