Generalized Wasserstein Flow Matching: Transport Plans, Everywhere, All at Once
Fuente:
arXiv
Guardado en:
| Autores principales: | Piening, Moritz, Duong, Richard, Steidl, Gabriele |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Neural Wasserstein Gradient Flows for Maximum Mean Discrepancies with Riesz Kernels
por: Altekrüger, Fabian, et al.
Publicado: (2023)
por: Altekrüger, Fabian, et al.
Publicado: (2023)
Slicing Wasserstein Over Wasserstein Via Functional Optimal Transport
por: Piening, Moritz, et al.
Publicado: (2025)
por: Piening, Moritz, et al.
Publicado: (2025)
Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching
por: Chemseddine, Jannis, et al.
Publicado: (2024)
por: Chemseddine, Jannis, et al.
Publicado: (2024)
Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel
por: Hagemann, Paul, et al.
Publicado: (2023)
por: Hagemann, Paul, et al.
Publicado: (2023)
A Novel Sliced Fused Gromov-Wasserstein Distance
por: Piening, Moritz, et al.
Publicado: (2025)
por: Piening, Moritz, et al.
Publicado: (2025)
Wasserstein Gradient Flows of the Discrepancy with Distance Kernel on the Line
por: Hertrich, Johannes, et al.
Publicado: (2023)
por: Hertrich, Johannes, et al.
Publicado: (2023)
Wasserstein Steepest Descent Flows of Discrepancies with Riesz Kernels
por: Hertrich, Johannes, et al.
Publicado: (2022)
por: Hertrich, Johannes, et al.
Publicado: (2022)
Flow Matching: Markov Kernels, Stochastic Processes and Transport Plans
por: Wald, Christian, et al.
Publicado: (2025)
por: Wald, Christian, et al.
Publicado: (2025)
Wasserstein Formulation of Reinforcement Learning. An Optimal Transport Perspective on Policy Optimization
por: Dus, Mathias
Publicado: (2026)
por: Dus, Mathias
Publicado: (2026)
Wasserstein Convergence of Score-based Generative Models under Semiconvexity and Discontinuous Gradients
por: Bruno, Stefano, et al.
Publicado: (2025)
por: Bruno, Stefano, et al.
Publicado: (2025)
Provable Mixed-Noise Learning with Flow-Matching
por: Hagemann, Paul, et al.
Publicado: (2025)
por: Hagemann, Paul, et al.
Publicado: (2025)
Properties of Discrete Sliced Wasserstein Losses
por: Tanguy, Eloi, et al.
Publicado: (2023)
por: Tanguy, Eloi, et al.
Publicado: (2023)
Linear convergence of proximal descent schemes on the Wasserstein space
por: Lascu, Razvan-Andrei, et al.
Publicado: (2024)
por: Lascu, Razvan-Andrei, et al.
Publicado: (2024)
Convergence of SGD for Training Neural Networks with Sliced Wasserstein Losses
por: Tanguy, Eloi
Publicado: (2023)
por: Tanguy, Eloi
Publicado: (2023)
Stochastic Inverse Problem: stability, regularization and Wasserstein gradient flow
por: Li, Qin, et al.
Publicado: (2024)
por: Li, Qin, et al.
Publicado: (2024)
Learning-Based Pricing and Matching for Two-Sided Queues
por: Yang, Zixian, et al.
Publicado: (2024)
por: Yang, Zixian, et al.
Publicado: (2024)
Designing Algorithms for Entropic Optimal Transport from an Optimisation Perspective
por: Srinivasan, Vishwak, et al.
Publicado: (2025)
por: Srinivasan, Vishwak, et al.
Publicado: (2025)
Neural Sampling from Boltzmann Densities: Fisher-Rao Curves in the Wasserstein Geometry
por: Chemseddine, Jannis, et al.
Publicado: (2024)
por: Chemseddine, Jannis, et al.
Publicado: (2024)
The geometry of financial institutions -- Wasserstein clustering of financial data
por: Riess, Lorenz, et al.
Publicado: (2023)
por: Riess, Lorenz, et al.
Publicado: (2023)
Controlling the Flow: Stability and Convergence for Stochastic Gradient Descent with Decaying Regularization
por: Kassing, Sebastian, et al.
Publicado: (2025)
por: Kassing, Sebastian, et al.
Publicado: (2025)
Robust $Q$-learning Algorithm for Markov Decision Processes under Wasserstein Uncertainty
por: Neufeld, Ariel, et al.
Publicado: (2022)
por: Neufeld, Ariel, et al.
Publicado: (2022)
Wasserstein Contraction of Coordinate Ascent Variational Inference
por: Caprio, Rocco, et al.
Publicado: (2026)
por: Caprio, Rocco, et al.
Publicado: (2026)
Diffusion-Based Stochastic Operator Networks for Uncertainty Quantification in Stochastic Partial Differential Equations
por: Huynh, Phuoc-Toan, et al.
Publicado: (2026)
por: Huynh, Phuoc-Toan, et al.
Publicado: (2026)
A Generalization Result for Convergence in Learning-to-Optimize
por: Sucker, Michael, et al.
Publicado: (2024)
por: Sucker, Michael, et al.
Publicado: (2024)
Stochastic Optimal Control Matching
por: Domingo-Enrich, Carles, et al.
Publicado: (2023)
por: Domingo-Enrich, Carles, et al.
Publicado: (2023)
ODE approximation for the Adam algorithm: General and overparametrized setting
por: Dereich, Steffen, et al.
Publicado: (2025)
por: Dereich, Steffen, et al.
Publicado: (2025)
Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise
por: Agrawal, Shubhada, et al.
Publicado: (2026)
por: Agrawal, Shubhada, et al.
Publicado: (2026)
Smoothed Distance Kernels for MMDs and Applications in Wasserstein Gradient Flows
por: Rux, Nicolaj, et al.
Publicado: (2025)
por: Rux, Nicolaj, et al.
Publicado: (2025)
Constrained Density Estimation via Optimal Transport
por: Hu, Yinan, et al.
Publicado: (2026)
por: Hu, Yinan, et al.
Publicado: (2026)
Dynamical Measure Transport and Neural PDE Solvers for Sampling
por: Sun, Jingtong, et al.
Publicado: (2024)
por: Sun, Jingtong, et al.
Publicado: (2024)
Flowing Datasets with Wasserstein over Wasserstein Gradient Flows
por: Bonet, Clément, et al.
Publicado: (2025)
por: Bonet, Clément, et al.
Publicado: (2025)
Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need
por: Yang, Shangda, et al.
Publicado: (2024)
por: Yang, Shangda, et al.
Publicado: (2024)
HOT-POT: Optimal Transport for Sparse Stereo Matching
por: Clerc, Antonin, et al.
Publicado: (2026)
por: Clerc, Antonin, et al.
Publicado: (2026)
Probabilistic Geometric Alignment via Bayesian Latent Transport for Domain-Adaptive Foundation Models
por: Aueawatthanaphisut, Aueaphum, et al.
Publicado: (2026)
por: Aueawatthanaphisut, Aueaphum, et al.
Publicado: (2026)
Efficient Risk-sensitive Planning via Entropic Risk Measures
por: Marthe, Alexandre, et al.
Publicado: (2025)
por: Marthe, Alexandre, et al.
Publicado: (2025)
On propagation of chaos for the Fisher-Rao gradient flow in entropic mean-field optimization
por: Lazić, Petra, et al.
Publicado: (2026)
por: Lazić, Petra, et al.
Publicado: (2026)
Value Mirror Descent for Reinforcement Learning
por: Jia, Zhichao, et al.
Publicado: (2026)
por: Jia, Zhichao, et al.
Publicado: (2026)
Bandit Allocational Instability
por: Chen, Yilun, et al.
Publicado: (2026)
por: Chen, Yilun, et al.
Publicado: (2026)
Model Predictive Control is almost Optimal for Heterogeneous Restless Multi-armed Bandits
por: Narasimha, Dheeraj, et al.
Publicado: (2025)
por: Narasimha, Dheeraj, et al.
Publicado: (2025)
Non-convex entropic mean-field optimization via Best Response flow
por: Lascu, Razvan-Andrei, et al.
Publicado: (2025)
por: Lascu, Razvan-Andrei, et al.
Publicado: (2025)
Ejemplares similares
-
Neural Wasserstein Gradient Flows for Maximum Mean Discrepancies with Riesz Kernels
por: Altekrüger, Fabian, et al.
Publicado: (2023) -
Slicing Wasserstein Over Wasserstein Via Functional Optimal Transport
por: Piening, Moritz, et al.
Publicado: (2025) -
Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching
por: Chemseddine, Jannis, et al.
Publicado: (2024) -
Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel
por: Hagemann, Paul, et al.
Publicado: (2023) -
A Novel Sliced Fused Gromov-Wasserstein Distance
por: Piening, Moritz, et al.
Publicado: (2025)