Cover Image

A Unified Framework for Data-Free One-Step Sampling via Wasserstein Gradient Flows

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Chenguang, Yu, Tianshu
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Order Matters in Retrosynthesis: Structure-aware Generation via Reaction-Center-Guided Discrete Flow Matching
by: Wang, Chenguang, et al.
Published: (2026)

Drifting Field Policy: A One-Step Generative Policy via Wasserstein Gradient Flow
by: Koo, Juil, et al.
Published: (2026)

Towards Principled Task Grouping for Multi-Task Learning
by: Wang, Chenguang, et al.
Published: (2024)

Incomplete Data, Complete Dynamics: A Diffusion Approach
by: Zhou, Zihan, et al.
Published: (2025)

Flowing Datasets with Wasserstein over Wasserstein Gradient Flows
by: Bonet, Clément, et al.
Published: (2025)

Are Your Generated Instances Truly Useful? GenBench-MILP: A Benchmark Suite for MILP Instance Generation
by: Luo, Yidong, et al.
Published: (2025)

Gradient Flow Drifting: Generative Modeling via Wasserstein Gradient Flows of KDE-Approximated Divergences
by: Cao, Jiarui, et al.
Published: (2026)

Efficient Training of Multi-task Neural Solver for Combinatorial Optimization
by: Wang, Chenguang, et al.
Published: (2023)

Beyond the Heatmap: A Rigorous Evaluation of Component Impact in MCTS-Based TSP Solvers
by: Pan, Xuanhao, et al.
Published: (2024)

Truncated Rectified Flow Policy for Reinforcement Learning with One-Step Sampling
by: Zhou, Xubin, et al.
Published: (2026)

Rethinking the Diffusion Models for Numerical Tabular Data Imputation from the Perspective of Wasserstein Gradient Flow
by: Chen, Zhichao, et al.
Published: (2024)

On the Wasserstein Gradient Flow Interpretation of Drifting Models
by: Gretton, Arthur, et al.
Published: (2026)

Multi-Objective Optimization via Wasserstein-Fisher-Rao Gradient Flow
by: Ren, Yinuo, et al.
Published: (2023)

One-Step Diffusion Samplers via Self-Distillation and Deterministic Flow
by: Jutras-Dube, Pascal, et al.
Published: (2025)

Functional Gradient Flows for Constrained Sampling
by: Zhang, Shiyue, et al.
Published: (2024)

One-Step Flow Policy Mirror Descent
by: Chen, Tianyi, et al.
Published: (2025)

On the Information Processing of One-Dimensional Wasserstein Distances with Finite Samples
by: Jang, Cheongjae, et al.
Published: (2025)

Generative Path-Finding Method for Wasserstein Gradient Flow
by: Liu, Chengyu, et al.
Published: (2026)

Wasserstein Gradient Flows for Scalable and Regularized Barycenter Computation
by: Montesuma, Eduardo Fernandes, et al.
Published: (2025)

Generative Modeling under Non-Monotone MAR Missingness via Approximate Wasserstein Gradient Flows
by: Kremling, Gitte, et al.
Published: (2026)

Understanding Oversmoothing in Diffusion-Based GNNs From the Perspective of Operator Semigroup Theory
by: Zhao, Weichen, et al.
Published: (2024)

Accelerated Multiple Wasserstein Gradient Flows for Multi-objective Distributional Optimization
by: Nguyen, Dai Hai, et al.
Published: (2026)

Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference
by: Nguyen, Dai Hai, et al.
Published: (2023)

DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient Flows
by: Geuter, Jonathan, et al.
Published: (2025)

A Particle-Flow Algorithm for Free-Support Wasserstein Barycenters
by: You, Kisung
Published: (2025)

A Unifying View of Variational Generative Wasserstein Flows
by: Caucheteux, Paul, et al.
Published: (2026)

Importance Weighted Score Matching for Diffusion Samplers with Enhanced Mode Coverage
by: Wang, Chenguang, et al.
Published: (2025)

Boosting Maximum Entropy Reinforcement Learning via One-Step Flow Matching
by: Li, Zeqiao, et al.
Published: (2026)

Wasserstein Gradient Flows for Batch Bayesian Optimal Experimental Design
by: Sharrock, Louis
Published: (2026)

Differentially Private Gradient Flow based on the Sliced Wasserstein Distance
by: Sebag, Ilana, et al.
Published: (2023)

Secrets of GFlowNets' Learning Behavior: A Theoretical Study
by: Yu, Tianshu
Published: (2025)

Learn to Evolve: Self-supervised Neural JKO Operator for Wasserstein Gradient Flow
by: Feng, Xue, et al.
Published: (2026)

Gradient Flows and Riemannian Structure in the Gromov-Wasserstein Geometry
by: Zhang, Zhengxin, et al.
Published: (2024)

You Only Look One Step: Accelerating Backpropagation in Diffusion Sampling with Gradient Shortcuts
by: Dou, Hongkun, et al.
Published: (2025)

Tyche: One Step Flow for Efficient Probabilistic Weather Forecasting
by: Xu, Fan, et al.
Published: (2026)

A Unified Framework for Diffusion Bridge Problems: Flow Matching and Schrödinger Matching into One
by: Kim, Minyoung
Published: (2025)

Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning
by: Matsubara, Takuo
Published: (2024)

Wasserstein Proximal Policy Gradient
by: Zhu, Zhaoyu, et al.
Published: (2026)

Smoothed Distance Kernels for MMDs and Applications in Wasserstein Gradient Flows
by: Rux, Nicolaj, et al.
Published: (2025)

A Computational Framework for Solving Wasserstein Lagrangian Flows
by: Neklyudov, Kirill, et al.
Published: (2023)