Results 61 to 70 of about 24,182 (212)
Bounding adapted Wasserstein metrics
The Wasserstein distance $\mathcal{W}_p$ is an important instance of an optimal transport cost. Its numerous mathematical properties as well as applications to various fields such as mathematical finance and statistics have been well studied in recent years.
Blanchet, Jose +3 more
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Distributionally robust joint chance-constrained programming with Wasserstein metric
In this paper, we develop an exact reformulation and a deterministic approximation for distributionally robust joint chance-constrained programmings (DRCCPs) with a general class of convex uncertain constraints under data-driven Wasserstein ambiguity sets.
Gu, Yining, Wang, Yanjun
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Covariance Structure Modeling of Engineering Demand Parameters in Cloud‐Based Seismic Analysis
ABSTRACT Probabilistic seismic demand modeling aims to estimate structural demand as a function of ground motion intensity—a critical stage in seismic risk assessment. Although many models exist to describe the structural demand, few consider the covariance among engineering demand parameters, potentially overlooking a key factor in improving the ...
Archie Rudman +3 more
wiley +1 more source
Federated Learning (FL) enhances privacy but remains vulnerable to model poisoning attacks, where an adversary manipulates client models to upload poisoned updates during training, thereby compromising the overall FL model.
Suzan Almutairi, Ahmed Barnawi
doaj +1 more source
A Lack of Ricci Bounds for the Entropic Measure on Wasserstein space over the Interval
This is a condensed form of the author's essay, which can be found at [arXiv:1105.2883]. We prove that the entropic measure constructed by von Renesse-Sturm over Wasserstein space on the unit interval (probability measures on the unit interval equipped ...
Chodosh +18 more
core +1 more source
Metric Currents and Geometry of Wasserstein Spaces
We investigate some geometric aspects of Wasserstein spaces through the continuity equation as worked out in mass transportation theory. By defining a suitable homology on the flat torus \mathbb T^n , we prove that the space
openaire +2 more sources
Permutation invariant networks to learn Wasserstein metrics
Understanding the space of probability measures on a metric space equipped with a Wasserstein distance is one of the fundamental questions in mathematical analysis. The Wasserstein metric has received a lot of attention in the machine learning community especially for its principled way of comparing distributions.
Sehanobish, Arijit +2 more
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Extending the Wasserstein metric to positive measures
We define a metric in the space of positive finite positive measures that extends the 2-Wasserstein metric, i.e. its restriction to the set of probability measures is the 2-Wasserstein metric. We prove a dual and a dynamic formulation and extend the gradient flow machinery of the Wasserstein space. In addition, we relate the barycenter in this space to
Leblanc, Hugo +3 more
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Esterification enhances carotenoid retention during bread‐making
Abstract BACKGROUND Carotenoids are plant‐derived antioxidants that contribute to human health and represent key quality traits in wheat‐based foods. However, they are highly unstable and prone to degradation during processing. Xanthophyll esterification has been identified as a natural mechanism that enhances carotenoid stability during grain storage.
María D Requena‐Ramírez +3 more
wiley +1 more source
A View on Optimal Transport from Noncommutative Geometry
We discuss the relation between the Wasserstein distance of order 1 between probability distributions on a metric space, arising in the study of Monge-Kantorovich transport problem, and the spectral distance of noncommutative geometry.
Francesco D'Andrea, Pierre Martinetti
doaj +1 more source

