JKO
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Related Articles from SNS
A Unifying View of Variational Generative Wasserstein Flows
arXiv:2605.31369v1 Announce Type: new Abstract: Many modern generative models can be viewed as minimizing divergences between probability distributions, yet they rely on different algorithmic and geometric principles. Wasserstein gradient flows provide a continuous-time formulation for optimizing over distributions, and can be approximated through their implicit discretization via the Jordan-Kinderlehrer-Otto (JKO) scheme. In this work, we present a unified theoretical framework for...
Variable-preconditioned transformed primal-dual method for generalized Wasserstein Gradient Flows
arXiv:2509.15385v3 Announce Type: replace Abstract: We propose a Variable-Preconditioned Transformed Primal-Dual (VPTPD) method for solving generalized Wasserstein gradient flows based on the structure-preserving JKO scheme. This is a nontrivial extension of the TPD method [Chen et al. incorporating proximal splitting techniques to address the challenges arising from the nonsmoothness of the objective function.
Graph Energy Matching: Transport-Aligned Energy-Based Modeling for Graph Generation
arXiv:2603.23398v2 Announce Type: replace Abstract: Generative modeling of discrete data, such as graphs, underpins many scientific and industrial applications, including molecular discovery and materials design. In these domains, probabilistic inference is particularly valuable, as it enables composable generation and principled incorporation of desired constraints, such as structural or functional properties. Energy-based models naturally support this goal by capturing relative likelihoods...
Generative Drifting is Secretly Score Matching: a Spectral and Variational Perspective
Announce Type: replace Abstract: Generative Modeling via Drifting~\citep{deng2026drifting} has recently achieved state-of-the-art one-step image generation through a kernel-based drift operator, yet its success is largely empirical and its theoretical foundations remain poorly understood. We observe that \emph{under a Gaussian kernel, the drift operator is exactly a score difference on smoothed distributions}. This answers three questions left open in the original work: (1) whether a...
Graph Energy Matching: Transport-Aligned Energy-Based Modeling for Graph Generation
Announce Type: replace Abstract: Generative modeling of discrete data, such as graphs, underpins many scientific and industrial applications, including molecular discovery and materials design. In these domains, probabilistic inference is particularly valuable, as it enables composable generation and principled incorporation of desired constraints, such as structural or functional properties. Energy-based models naturally support this goal by capturing relative likelihoods and enabling...