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Found 6 papers, 3 papers with code
Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference
Notably, the optimization techniques, namely black-box VI and natural-gradient VI, can be reinterpreted as specific instances of the proposed Wasserstein gradient descent.
Moreau-Yoshida Variational Transport: A General Framework For Solving Regularized Distributional Optimization Problems
We consider a general optimization problem of minimizing a composite objective functional defined over a class of probability distributions.
A Particle-Based Algorithm for Distributional Optimization on \textit{Constrained Domains} via Variational Transport and Mirror Descent
We consider the optimization problem of minimizing an objective functional, which admits a variational form and is defined over probability distributions on the constrained domain, which poses challenges to both theoretical analysis and algorithmic design.
On a linear fused Gromov-Wasserstein distance for graph structured data
We present a framework for embedding graph structured data into a vector space, taking into account node features and topology of a graph into the optimal transport (OT) problem.
Learning subtree pattern importance for Weisfeiler-Lehmanbased graph kernels
Graph is an usual representation of relational data, which are ubiquitous in manydomains such as molecules, biological and social networks.
A generative model for molecule generation based on chemical reaction trees
We propose a generative model to generate molecules via multi-step chemical reaction trees.
