Zobrazeno 1 - 10
of 130
pro vyhledávání: '"Denis, Christophe"'
We are interested in the problem of classifying Multivariate Hawkes Processes (MHP) paths coming from several classes. MHP form a versatile family of point processes that models interactions between connected individuals within a network. In this pap
Externí odkaz:
http://arxiv.org/abs/2407.11455
We study the multiclass classification problem where the features come from the mixture of time-homogeneous diffusions. Specifically, the classes are discriminated by their drift functions while the diffusion coefficient is common to all classes and
Externí odkaz:
http://arxiv.org/abs/2212.10259
We tackle the problem of building a prediction interval in heteroscedastic Gaussian regression. We focus on prediction intervals with constrained expected length in order to guarantee interpretability of the output. In this framework, we derive a clo
Externí odkaz:
http://arxiv.org/abs/2209.03589
Active learning is a paradigm of machine learning which aims at reducing the amount of labeled data needed to train a classifier. Its overall principle is to sequentially select the most informative data points, which amounts to determining the uncer
Externí odkaz:
http://arxiv.org/abs/2208.14682
Algorithmic Fairness is an established area of machine learning, willing to reduce the influence of hidden bias in the data. Yet, despite its wide range of applications, very few works consider the multi-class classification setting from the fairness
Externí odkaz:
http://arxiv.org/abs/2109.13642
Multi-class classification problem is among the most popular and well-studied statistical frameworks. Modern multi-class datasets can be extremely ambiguous and single-output predictions fail to deliver satisfactory performance. By allowing predictor
Externí odkaz:
http://arxiv.org/abs/2102.12318
Publikováno v:
NeurIPS 2020 - 34th Conference on Neural Information Processing Systems, Dec 2020, Vancouver / Virtuel, Canada
We investigate the problem of regression where one is allowed to abstain from predicting. We refer to this framework as regression with reject option as an extension of classification with reject option. In this context, we focus on the case where th
Externí odkaz:
http://arxiv.org/abs/2006.16597
We study the problem of learning a real-valued function that satisfies the Demographic Parity constraint. It demands the distribution of the predicted output to be independent of the sensitive attribute. We consider the case that the sensitive attrib
Externí odkaz:
http://arxiv.org/abs/2006.07286
Publikováno v:
NeurIPS 2019 - 33th Annual Conference on Neural Information Processing Systems, Dec 2019, Vancouver, Canada
We study the problem of fair binary classification using the notion of Equal Opportunity. It requires the true positive rate to distribute equally across the sensitive groups. Within this setting we show that the fair optimal classifier is obtained b
Externí odkaz:
http://arxiv.org/abs/1906.05082
In this work we study the semi-supervised framework of confidence set classification with controlled expected size in minimax settings. We obtain semi-supervised minimax rates of convergence under the margin assumption and a H{\"o}lder condition on t
Externí odkaz:
http://arxiv.org/abs/1904.12527