Literatura académica sobre el tema "Distances de Wasserstein"
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Artículos de revistas sobre el tema "Distances de Wasserstein"
Solomon, Justin, Fernando de Goes, Gabriel Peyré, Marco Cuturi, Adrian Butscher, Andy Nguyen, Tao Du y Leonidas Guibas. "Convolutional wasserstein distances". ACM Transactions on Graphics 34, n.º 4 (27 de julio de 2015): 1–11. http://dx.doi.org/10.1145/2766963.
Texto completoKindelan Nuñez, Rolando, Mircea Petrache, Mauricio Cerda y Nancy Hitschfeld. "A Class of Topological Pseudodistances for Fast Comparison of Persistence Diagrams". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 12 (24 de marzo de 2024): 13202–10. http://dx.doi.org/10.1609/aaai.v38i12.29220.
Texto completoPanaretos, Victor M. y Yoav Zemel. "Statistical Aspects of Wasserstein Distances". Annual Review of Statistics and Its Application 6, n.º 1 (7 de marzo de 2019): 405–31. http://dx.doi.org/10.1146/annurev-statistics-030718-104938.
Texto completoKelbert, Mark. "Survey of Distances between the Most Popular Distributions". Analytics 2, n.º 1 (1 de marzo de 2023): 225–45. http://dx.doi.org/10.3390/analytics2010012.
Texto completoVayer, Titouan, Laetitia Chapel, Remi Flamary, Romain Tavenard y Nicolas Courty. "Fused Gromov-Wasserstein Distance for Structured Objects". Algorithms 13, n.º 9 (31 de agosto de 2020): 212. http://dx.doi.org/10.3390/a13090212.
Texto completoBelili, Nacereddine y Henri Heinich. "Distances de Wasserstein et de Zolotarev". Comptes Rendus de l'Académie des Sciences - Series I - Mathematics 330, n.º 9 (mayo de 2000): 811–14. http://dx.doi.org/10.1016/s0764-4442(00)00274-3.
Texto completoPeyre, Rémi. "Comparison between W2 distance and Ḣ−1 norm, and Localization of Wasserstein distance". ESAIM: Control, Optimisation and Calculus of Variations 24, n.º 4 (octubre de 2018): 1489–501. http://dx.doi.org/10.1051/cocv/2017050.
Texto completoTong, Qijun y Kei Kobayashi. "Entropy-Regularized Optimal Transport on Multivariate Normal and q-normal Distributions". Entropy 23, n.º 3 (3 de marzo de 2021): 302. http://dx.doi.org/10.3390/e23030302.
Texto completoBeier, Florian, Robert Beinert y Gabriele Steidl. "Multi-marginal Gromov–Wasserstein transport and barycentres". Information and Inference: A Journal of the IMA 12, n.º 4 (18 de septiembre de 2023): 2720–52. http://dx.doi.org/10.1093/imaiai/iaad041.
Texto completoZhang, Zhonghui, Huarui Jing y Chihwa Kao. "High-Dimensional Distributionally Robust Mean-Variance Efficient Portfolio Selection". Mathematics 11, n.º 5 (6 de marzo de 2023): 1272. http://dx.doi.org/10.3390/math11051272.
Texto completoTesis sobre el tema "Distances de Wasserstein"
Boissard, Emmanuel. "Problèmes d'interaction discret-continu et distances de Wasserstein". Toulouse 3, 2011. http://thesesups.ups-tlse.fr/1389/.
Texto completoWe study several problems of approximation using tools from Optimal Transportation theory. The family of Wasserstein metrics are used to provide error bounds for particular approximation of some Partial Differential Equations. They also come into play as natural measures of distorsion for quantization and clustering problems. A problem related to these questions is to estimate the speed of convergence in the empirical law of large numbers for these distorsions. The first part of this thesis provides non-asymptotic bounds, notably in infinite-dimensional Banach spaces, as well as in cases where independence is removed. The second part is dedicated to the study of two models from the modelling of animal displacement. A new individual-based model for ant trail formation is introduced, and studied through numerical simulations and kinetic formulation. We also study a variant of the Cucker-Smale model of bird flock motion : we establish well-posedness of the associated Vlasov-type transport equation as well as long-time behaviour results. In a third part, we study some statistical applications of the notion of barycenter in Wasserstein space recently introduced by M. Agueh and G. Carlier
Schrieber, Jörn [Verfasser], Dominic [Akademischer Betreuer] Schuhmacher, Dominic [Gutachter] Schuhmacher y Anita [Gutachter] Schöbel. "Algorithms for Optimal Transport and Wasserstein Distances / Jörn Schrieber ; Gutachter: Dominic Schuhmacher, Anita Schöbel ; Betreuer: Dominic Schuhmacher". Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2019. http://d-nb.info/1179449304/34.
Texto completoSEGUY, Vivien Pierre François. "Measure Transport Approaches for Data Visualization and Learning". Kyoto University, 2018. http://hdl.handle.net/2433/233857.
Texto completoGairing, Jan, Michael Högele, Tetiana Kosenkova y Alexei Kulik. "On the calibration of Lévy driven time series with coupling distances : an application in paleoclimate". Universität Potsdam, 2014. http://opus.kobv.de/ubp/volltexte/2014/6978/.
Texto completoFlenghi, Roberta. "Théorème de la limite centrale pour des fonctionnelles non linéaires de la mesure empirique et pour le rééchantillonnage stratifié". Electronic Thesis or Diss., Marne-la-vallée, ENPC, 2023. http://www.theses.fr/2023ENPC0051.
Texto completoThis thesis is dedicated to the central limit theorem which is one of the two fundamental limit theorems in probability theory with the strong law of large numbers.The central limit theorem which is well known for linear functionals of the empirical measure of independent and identically distributed random vectors, has recently been extended to non-linear functionals. The main tool permitting this extension is the linear functional derivative, one of the notions of derivation on the Wasserstein space of probability measures.We generalize this extension by first relaxing the equal distribution assumptionand then the independence property to be able to deal with the successive values of an ergodic Markov chain.In the second place, we focus on the stratified resampling mechanism.This is one of the resampling schemes commonly used in particle filters. We prove a central limit theorem for the first resampling according to this mechanism under the assumption that the initial positions are independent and identically distributed and the weights proportional to a positive function of the positions such that the image of their common distribution by this function has a non zero component absolutely continuous with respect to the Lebesgue measure. This result relies on the convergence in distribution of the fractional part of partial sums of the normalized weights to some random variable uniformly distributed on [0,1]. More generally, we prove the joint convergence in distribution of q variables modulo one obtained as partial sums of a sequence of i.i.d. square integrable random variables multiplied by a common factor given by some function of an empirical mean of the same sequence. The limit is uniformly distributed over [dollar][0,1]^q[dollar]. To deal with the coupling introduced by the common factor, we assume that the common distribution of the random variables has a non zero component absolutely continuous with respect to the Lebesgue measure, so that the convergence in the central limit theorem for this sequence holds in total variation distance.Under the conjecture that the convergence in distribution of fractional parts to some uniform random variable remains valid at the next steps of a particle filter which alternates selections according to the stratified resampling mechanism and mutations according to Markov kernels, we provide an inductive formula for the asymptotic variance of the resampled population after n steps. We perform numerical experiments which support the validity of this formula
Bobbia, Benjamin. "Régression quantile extrême : une approche par couplage et distance de Wasserstein". Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCD043.
Texto completoThis work is related with the estimation of conditional extreme quantiles. More precisely, we estimate high quantiles of a real distribution conditionally to the value of a covariate, potentially in high dimension. A such estimation is made introducing the proportional tail model. This model is studied with coupling methods. The first is an empirical processes based method whereas the second is focused on transport and optimal coupling. We provide estimators of both quantiles and model parameters, we show their asymptotic normality with our coupling methods. We also provide a validation procedure for proportional tail model. Moreover, we develop the second approach in the general framework of univariate extreme value theory
Liu, Lu. "A Risk-Oriented Clustering Approach for Asset Categorization and Risk Measurement". Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39444.
Texto completoLescornel, Hélène. "Covariance estimation and study of models of deformations between distributions with the Wasserstein distance". Toulouse 3, 2014. http://www.theses.fr/2014TOU30045.
Texto completoThe first part of this thesis concerns the covariance estimation of non stationary processes. We are estimating the covariance in different vectorial spaces of matrices. In Chapter 3, we give a model selection procedure by minimizing a penalized criterion and using concentration inequalities, and Chapter 4 presents an Unbiased Risk Estimation method. In both cases we give oracle inequalities. The second part deals with the study of models of deformation between distributions. We assume that we observe a random quantity epsilon through a deformation function. The importance of the deformation is represented by a parameter theta that we aim to estimate. We present several methods of estimation based on the Wasserstein distance by aligning the distributions of the observations to recover the deformation parameter. In the case of real random variables, Chapter 7 presents properties of consistency for a M-estimator and its asymptotic distribution. We use Hadamard differentiability techniques to apply a functional Delta method. Chapter 8 concerns a Robbins-Monro estimator for the deformation parameter and presents properties of convergence for a kernel estimator of the density of the variable epsilon obtained with the observations. The model is generalized to random variables in complete metric spaces in Chapter 9. Then, in the aim to build a goodness of fit test, Chapter 10 gives results on the asymptotic distribution of a test statistic
Boistard, Hélène. "Eficacia asintotica tests relacionados con el estadística de Wasserstein". Toulouse 3, 2007. http://www.theses.fr/2007TOU30155.
Texto completoThe goodness of fit test based on the Wasserstein distance is a test which is well adapted to location-scale families. The asymptotic distribution under the null hypothesis has been known since the works by del Barrio et al. (1999, 2000). The subject of this thesis is the study of the asymptotic power of this test and of some related tests, owing to several efficiency criteria. In the first chapter, a short introduction presents the problem and the tools to be used. The second chapter is devoted to the the proof of some asymptotic results for multiple integrals with respect to the empirical process. These statistics are strongly related to U-statistics, but they permit an important simplification of the classical hypotheses to establish the asymptotic distribution under the null hypothesis, under contiguous alternative and for the bootstrap. In the third chapter, we prove that the Wasserstein test statistic is equivalent to a test based on the double integral with respect to the empirical process. This allows us to apply to this test the results of the previous chapter, and to obtain some information about its asymptotic efficiency in the framework of Gaussian shift experiments. .
Lebrat, Léo. "Projection au sens de Wasserstein 2 sur des espaces structurés de mesures". Thesis, Toulouse, INSA, 2019. http://www.theses.fr/2019ISAT0035.
Texto completoThis thesis focuses on the approximation for the 2-Wasserstein metric of probability measures by structured measures. The set of structured measures under consideration is made of consistent discretizations of measures carried by a smooth curve with a bounded speed and acceleration. We compare two different types of approximations of the curve: piecewise constant and piecewise linear. For each of these methods, we develop fast and scalable algorithms to compute the 2-Wasserstein distance between a given measure and the structured measure. The optimization procedure reveals new theoretical and numerical challenges, it consists of two steps: first the computation of the 2-Wasserstein distance, second the optimization of the parameters of structure. This work is initially motivated by the design of trajectories in MRI acquisition, however we provide new applications of these methods
Libros sobre el tema "Distances de Wasserstein"
Computational Inversion with Wasserstein Distances and Neural Network Induced Loss Functions. [New York, N.Y.?]: [publisher not identified], 2022.
Buscar texto completoAn Invitation to Optimal Transport, Wasserstein Distances, and Gradient Flows. European Mathematical Society, 2021.
Buscar texto completoCapítulos de libros sobre el tema "Distances de Wasserstein"
Bachmann, Fynn, Philipp Hennig y Dmitry Kobak. "Wasserstein t-SNE". En Machine Learning and Knowledge Discovery in Databases, 104–20. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26387-3_7.
Texto completoVillani, Cédric. "The Wasserstein distances". En Grundlehren der mathematischen Wissenschaften, 93–111. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-71050-9_6.
Texto completoBarbe, Amélie, Marc Sebban, Paulo Gonçalves, Pierre Borgnat y Rémi Gribonval. "Graph Diffusion Wasserstein Distances". En Machine Learning and Knowledge Discovery in Databases, 577–92. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67661-2_34.
Texto completoJacobs, Bart. "Drawing from an Urn is Isometric". En Lecture Notes in Computer Science, 101–20. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57228-9_6.
Texto completoSantambrogio, Filippo. "Wasserstein distances and curves in the Wasserstein spaces". En Optimal Transport for Applied Mathematicians, 177–218. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20828-2_5.
Texto completoÖcal, Kaan, Ramon Grima y Guido Sanguinetti. "Wasserstein Distances for Estimating Parameters in Stochastic Reaction Networks". En Computational Methods in Systems Biology, 347–51. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31304-3_24.
Texto completoCarrillo, José Antonio, Young-Pil Choi y Maxime Hauray. "The derivation of swarming models: Mean-field limit and Wasserstein distances". En Collective Dynamics from Bacteria to Crowds, 1–46. Vienna: Springer Vienna, 2014. http://dx.doi.org/10.1007/978-3-7091-1785-9_1.
Texto completoHaeusler, Erich y David M. Mason. "Asymptotic Distributions of Trimmed Wasserstein Distances Between the True and the Empirical Distribution Function". En Stochastic Inequalities and Applications, 279–98. Basel: Birkhäuser Basel, 2003. http://dx.doi.org/10.1007/978-3-0348-8069-5_16.
Texto completoWalczak, Szymon M. "Wasserstein Distance". En SpringerBriefs in Mathematics, 1–10. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57517-9_1.
Texto completoBreiding, Paul, Kathlén Kohn y Bernd Sturmfels. "Wasserstein Distance". En Oberwolfach Seminars, 53–66. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51462-3_5.
Texto completoActas de conferencias sobre el tema "Distances de Wasserstein"
Lopez, Adrian Tovar y Varun Jog. "Generalization error bounds using Wasserstein distances". En 2018 IEEE Information Theory Workshop (ITW). IEEE, 2018. http://dx.doi.org/10.1109/itw.2018.8613445.
Texto completoMemoli, Facundo. "Spectral Gromov-Wasserstein distances for shape matching". En 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops. IEEE, 2009. http://dx.doi.org/10.1109/iccvw.2009.5457690.
Texto completoProssel, Dominik y Uwe D. Hanebeck. "Dirac Mixture Reduction Using Wasserstein Distances on Projected Cumulative Distributions". En 2022 25th International Conference on Information Fusion (FUSION). IEEE, 2022. http://dx.doi.org/10.23919/fusion49751.2022.9841286.
Texto completoSteuernagel, Simon, Aaron Kurda y Marcus Baum. "Point Cloud Registration based on Gaussian Mixtures and Pairwise Wasserstein Distances". En 2023 IEEE Symposium Sensor Data Fusion and International Conference on Multisensor Fusion and Integration (SDF-MFI). IEEE, 2023. http://dx.doi.org/10.1109/sdf-mfi59545.2023.10361440.
Texto completoPerkey, Scott, Ana Carvalho y Alberto Krone-Martins. "Using Fourier Coefficients and Wasserstein Distances to Estimate Entropy in Time Series". En 2023 IEEE 19th International Conference on e-Science (e-Science). IEEE, 2023. http://dx.doi.org/10.1109/e-science58273.2023.10254949.
Texto completoBarbe, Amelie, Paulo Goncalves, Marc Sebban, Pierre Borgnat, Remi Gribonval y Titouan Vayer. "Optimization of the Diffusion Time in Graph Diffused-Wasserstein Distances: Application to Domain Adaptation". En 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2021. http://dx.doi.org/10.1109/ictai52525.2021.00125.
Texto completoGarcia Ramirez, Jesus. "Which Kernels to Transfer in Deep Q-Networks?" En LatinX in AI at Neural Information Processing Systems Conference 2019. Journal of LatinX in AI Research, 2019. http://dx.doi.org/10.52591/lxai201912087.
Texto completoChoi, Youngwon y Joong-Ho Won. "Ornstein Auto-Encoders". En Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/301.
Texto completoKasai, Hiroyuki. "Multi-View Wasserstein Discriminant Analysis with Entropic Regularized Wasserstein Distance". En ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054427.
Texto completoSu, Yuxin, Shenglin Zhao, Xixian Chen, Irwin King y Michael Lyu. "Parallel Wasserstein Generative Adversarial Nets with Multiple Discriminators". En Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/483.
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