Distributionally robust learning-to-rank under the Wasserstein metric [PDF]
PLoS ONE, 2023 Despite their satisfactory performance, most existing listwise Learning-To-Rank (LTR) models do not consider the crucial issue of robustness. A data set can be contaminated in various ways, including human error in labeling or annotation, distributional ...
Shahabeddin Sotudian +2 more
doaj +6 more sources
Calculating the Wasserstein Metric-Based Boltzmann Entropy of a Landscape Mosaic [PDF]
Entropy, 2020 Shannon entropy is currently the most popular method for quantifying the disorder or information of a spatial data set such as a landscape pattern and a cartographic map.
Hong Zhang +4 more
doaj +2 more sources
Geometric Characteristics of the Wasserstein Metric on SPD(n) and Its Applications on Data Processing [PDF]
Entropy, 2021 The Wasserstein distance, especially among symmetric positive-definite matrices, has broad and deep influences on the development of artificial intelligence (AI) and other branches of computer science.
Yihao Luo +3 more
doaj +2 more sources
Nonnegative matrix factorization with Wasserstein metric-based regularization for enhanced text embedding. [PDF]
PLoS ONEText embedding plays a crucial role in natural language processing (NLP). Among various approaches, nonnegative matrix factorization (NMF) is an effective method for this purpose.
Mingming Li +3 more
doaj +2 more sources
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.
europepmc +3 more sources
Ensemble Riemannian data assimilation over the Wasserstein space [PDF]
Nonlinear Processes in Geophysics, 2021 In this paper, we present an ensemble data assimilation paradigm over a Riemannian manifold equipped with the Wasserstein metric. Unlike the Euclidean distance used in classic data assimilation methodologies, the Wasserstein metric can capture the ...
S. K. Tamang +6 more
doaj +1 more source
Wasserstein model reduction approach for parametrized flow problems in porous media [PDF]
ESAIM: Proceedings and Surveys, 2023 The aim of this work is to build a reduced order model for parametrized porous media equations. The main challenge of this type of problems is that the Kolmogorov width of the solution manifold typically decays quite slowly and thus makes usual linear ...
Battisti Beatrice +5 more
doaj +1 more source
Free complete Wasserstein algebras [PDF]
Logical Methods in Computer Science, 2018 We present an algebraic account of the Wasserstein distances $W_p$ on complete metric spaces, for $p \geq 1$. This is part of a program of a quantitative algebraic theory of effects in programming languages.
Radu Mardare +2 more
doaj +1 more source
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
openaire +2 more sources
(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
openaire +5 more sources