Results 11 to 20 of about 23,924 (195)
Distributionally robust mean-absolute deviation portfolio optimization using wasserstein metric. [PDF]
J Glob Optim, 2022 Data uncertainty has a great impact on portfolio selection. Based on the popular mean-absolute deviation (MAD) model, we investigate how to make robust portfolio decisions. In this paper, a novel Wasserstein metric-based data-driven distributionally robust mean-absolute deviation (DR-MAD) model is proposed.
Chen D, Wu Y, Li J, Ding X, Chen C.
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Nonlocal Wasserstein distance: metric and asymptotic properties
Calculus of Variations and Partial Differential Equations, 2023 AbstractThe seminal result of Benamou and Brenier provides a characterization of the Wasserstein distance as the path of the minimal action in the space of probability measures, where paths are solutions of the continuity equation and the action is the kinetic energy.
Dejan Slepčev, Andrew Warren
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(q,p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs [PDF]
Proceedings of the American Mathematical Society, 2019 Generative Adversial Networks (GANs) have made a major impact in computer vision and machine learning as generative models. Wasserstein GANs (WGANs) brought Optimal Transport (OT) theory into GANs, by minimizing the $1$-Wasserstein distance between model and data distributions as their objective function.
Mallasto, Anton +3 more
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Wasserstein distance and metric trees
L’Enseignement Mathématique, 2023 We study the Wasserstein (or earthmover) metric on the space P(X) of probability measures on a metric space X . We show that, if a finite metric space
Mathey-Prevot, Maxime, Valette, Alain
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Open-Set Signal Recognition Based on Transformer and Wasserstein Distance
Applied Sciences, 2023 Open-set signal recognition provides a new approach for verifying the robustness of models by introducing novel unknown signal classes into the model testing and breaking the conventional closed-set assumption, which has become very popular in real-world
Wei Zhang +4 more
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Well-posedness of a parabolic moving-boundary problem in the setting of Wasserstein gradient flows [PDF]
, 2010 We develop a gradient-flow framework based on the Wasserstein metric for a parabolic moving-boundary problem that models crystal dissolution and precipitation. In doing so we derive a new weak formulation for this moving-boundary problem and we show that
Peletier, Mark A., Portegies, Jacobus W.
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Deconvolution for the Wasserstein metric and geometric inference [PDF]
Electronic Journal of Statistics, 2011 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Caillerie, Claire +3 more
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A generalization of Hausdorff dimension applied to Hilbert cubes and Wasserstein spaces [PDF]
, 2012 A Wasserstein spaces is a metric space of sufficiently concentrated probability measures over a general metric space. The main goal of this paper is to estimate the largeness of Wasserstein spaces, in a sense to be precised.
BENOÎT KLOECKNER +6 more
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Big Data and Cognitive Computing, 2022 Large retail companies routinely gather huge amounts of customer data, which are to be analyzed at a low granularity. To enable this analysis, several Key Performance Indicators (KPIs), acquired for each customer through different channels are associated
Andrea Ponti +4 more
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Optimal Transport for Gaussian Mixture Models
IEEE Access, 2019 We introduce an optimal mass transport framework on the space of Gaussian mixture models. These models are widely used in statistical inference. Specifically, we treat the Gaussian mixture models as a submanifold of probability densities equipped with ...
Yongxin Chen +2 more
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