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fastfrechet: An R package for fast implementation of Fréchet regression with distributional responses. [PDF]
J Open Source SoftwCoulter A, Lee R, Gaynanova I.
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Why Wasserstein Metric Is Useful in Econometrics
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2023In many practical situations, we need to change the spatial distribution of some goods. In such situations, it is desirable to minimize the overall transportation costs. In the 1-D case, the smallest transportation cost of such a change is proportional to what is known as the Wasserstein metric.
Thach, Nguyen Ngoc +2 more
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Distributionally Robust Games: Wasserstein Metric
2018 International Joint Conference on Neural Networks (IJCNN), 2018Deep generative models are powerful but difficult to train due to its instability, saturation problem and high dimensional data distribution. This paper introduces a game theory framework with Wasserstein metric to train generative models, in which the unknown data distribution is learned by dynamically optimizing the worst-case payoff.
Jian Gao, Hamidou Tembine
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Frequency domain model validation in Wasserstein metric
2013 American Control Conference, 2013This paper connects the time-domain uncertainty propagation approach for model validation in Wasserstein distance 2W2, introduced by the authors in [1], with the frequency domain model validation in the same. To the best of our knowledge, this is the first frequency domain interpretation of Monge-Kantorovich optimal transport.
Abhishek Halder, Raktim Bhattacharya
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Unsupervised Ground Metric Learning using Wasserstein Eigenvectors
2022Optimal Transport (OT) defines geometrically meaningful "Wasserstein" distances, used in machine learning applications to compare probability distributions. However, a key bottleneck is the design of a "ground" cost which should be adapted to the task under study.
Huizing, Geert-Jan +2 more
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A New Interval Data Distance Based on the Wasserstein Metric
2008Interval data allow statistical units to be described by means of interval values, whereas their representation by single values appears to be too reductive or inconsistent, that is, unable to keep the uncertainty usually inherent to the observed data. In the present paper, we present a novel distance for interval data based on the Wasserstein distance
VERDE, Rosanna, IRPINO, Antonio
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Wasserstein Metric Attack on Person Re-identification
2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR), 2022Astha Verma +2 more
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Metric property of quantum Wasserstein divergences
Physical Review AGergely Bunth +3 more
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