4 citations
- CELESTE: statistique mathématique et apprentissageFR1 paper
- Centre de Recherche en Économie et StatistiqueFR1 paper
- Centre National de la Recherche ScientifiqueFR1 paper
- École Nationale de la Statistique et de l'Analyse de l'InformationFR1 paper
- ENSAE ParisFR1 paper
- Institut de Biologie ValroseFR1 paper
- Institut de Mécanique Céleste et de Calcul des ÉphéméridesFR1 paper
- Institut du ThoraxFR1 paper
- Institut Universitaire de Recherche CliniqueFR1 paper
- Laboratoire de Probabilités et Modèles AléatoiresFR1 paper
- Laboratoire de Probabilités, Statistique et ModélisationFR1 paper
- MAASAI: Modèles et algorithmes pour l'intelligence artificielleFR1 paper
4 papers
Seeded graph matching for the correlated Gaussian Wigner model via the projected power method
Ernesto Araya, Guillaume Braun, Hemant Tyagi
In the \emph{graph matching} problem we observe two graphs and the goal is to find an assignment (or matching) between their vertices such that some measure of edge agreement…
Model-based Clustering with Missing Not At Random Data
Aude Sportisse, Matthieu Marbac, Fabien Laporte +4
Model-based unsupervised learning, as any learning task, stalls as soon as missing data occurs. This is even more true when the missing data are informative, or said missing not at…
Upper and Lower Bounds on the Performance of Kernel PCA
Maxime Haddouche, Benjamin Guedj, John Shawe-Taylor
Principal Component Analysis (PCA) is a popular method for dimension reduction and has attracted an unfailing interest for decades. More recently, kernel PCA (KPCA) has emerged as…
Learning Binary Decision Trees by Argmin Differentiation
Valentina Zantedeschi, Matt J. Kusner, Vlad Niculae
We address the problem of learning binary decision trees that partition data for some downstream task. We propose to learn discrete parameters (i.e., for tree traversals and node p…