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Bregman divergence and density integration
Bregman divergence and density integrationIn this paper, a problem of integrating density functions is considered. First, the Bregman divergence in the space of positive finite measures is introduced, and properties of consistent subspaces and well-behaved parametric models associated with the ...
Fujimoto, Yu +5 more
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Symplectic Bregman Divergences. [PDF]
Entropy (Basel)We present a generalization of Bregman divergences in finite-dimensional symplectic vector spaces that we term symplectic Bregman divergences. Symplectic Bregman divergences are derived from a symplectic generalization of the Fenchel–Young inequality which relies on the notion of symplectic subdifferentials.
Nielsen F.
europepmc +6 more sources
Maximizing the Bregman divergence from a Bregman family [PDF]
Kybernetika, 2020 11 pages, 5 theorems, no ...
Johannes Rauh, Frantisek Matús
openaire +5 more sources
Comptes Rendus. Mécanique, 2023 With view on the context of convex thermomechanics, we propose tools based on the concept of Bregman divergence, a notion introduced in the 1960s and used in learning and optimization as well. This study is motivated by the need of “discrepancy measures”
Andrieux, Stéphane
doaj +1 more source
Hyperlink regression via Bregman divergence [PDF]
Neural Networks, 2020 41 pages, 14 ...
Akifumi Okuno, Hidetoshi Shimodaira
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Transport information Bregman divergences [PDF]
Information Geometry, 2021 Typos are ...
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On Voronoi Diagrams on the Information-Geometric Cauchy Manifolds
Entropy, 2020 We study the Voronoi diagrams of a finite set of Cauchy distributions and their dual complexes from the viewpoint of information geometry by considering the Fisher-Rao distance, the Kullback-Leibler divergence, the chi square divergence, and a flat ...
Frank Nielsen
doaj +1 more source
Learning Empirical Bregman Divergence for Uncertain Distance Representation [PDF]
, 2023 Deep metric learning techniques have been used for visual representation in various supervised and unsupervised learning tasks through learning embeddings of samples with deep networks. However, classic approaches, which employ a fixed distance metric as
Li, Zhiyuan +3 more
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Entropy, 2022 By calculating the Kullback–Leibler divergence between two probability measures belonging to different exponential families dominated by the same measure, we obtain a formula that generalizes the ordinary Fenchel–Young divergence.
Frank Nielsen
doaj +1 more source
An Objective Prior from a Scoring Rule
Entropy, 2021 In this paper, we introduce a novel objective prior distribution levering on the connections between information, divergence and scoring rules. In particular, we do so from the starting point of convex functions representing information in density ...
Stephen G. Walker, Cristiano Villa
doaj +1 more source