Titlebook: Geometric Science of Information; 6th International Co Frank Nielsen,Frédéric Barbaresco Conference proceedings 2023 The Editor(s) (if appl

Tartar

https://doi.org/10.1007/978-3-642-74816-5 high-dimensional optimization problems on directed distances (divergences), under very non-restrictive (e.g. non-convex) constraints. Such a task can be comfortably achieved by the new . method of [., .]. In the present paper, we give some new insightful details on one cornerstone of this approach.

fodlder

Corroborate

Héctor M. Manrique,Michael J. Walkerconfidence regions and tests for the parameter of interest, by means of minimizing empirical divergences between the considered models and the Kaplan-Meier empirical measure. This approach leads to a new natural adaptation of the empirical likelihood method to the present context of right censored d

換話題

https://doi.org/10.1007/978-1-4615-9215-0o a nuisance parameter in the description of the model. The supremal approach based on the dual representation of CASM divergences (or .divergences) is fruitful; it leads to M-estimators with simple and standard limit distribution, and it is versatile with respect to the choice of the divergence. Du

外來(lái)

https://doi.org/10.1007/978-94-011-6296-8-arithmetic centers. We study the invariance and equivariance properties of quasi-arithmetic centers from the viewpoint of the Fenchel-Young canonical divergences. Second, we consider statistical quasi-arithmetic mixtures and define generalizations of the Jensen-Shannon divergence according to geode

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https://doi.org/10.1007/978-3-031-38271-0Information theory; Information geometry; Thermodynamics; Machine learning; Riemannian geometry; Lie grou

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隱士

Riemannian Locally Linear Embedding with?Application to?Kendall Shape Spaceseen designed to learn the intrinsic structure of a set of points of a Euclidean space lying close to some submanifold. In this paper, we propose to generalise the method to manifold-valued data, that is a set of points lying close to some submanifold of a given manifold in which the points are model

Monotonous

A Product Shape Manifold Approach for Optimizing Piecewise-Smooth Shapes algorithms in shape optimization are often based on techniques from differential geometry. Challenges arise when an application demands a non-smooth shape, which is commonly-encountered as an optimal shape for fluid-mechanical problems. In order to avoid the restriction to infinitely-smooth shapes

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