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Found 8 papers, 1 papers with code
Path convergence of Markov chains on large graphs
In both cases we show that, as the size of the graph goes to infinity, the random trajectories of the stochastic processes converge to deterministic curves on the space of measure-valued graphons.
Wasserstein Mirror Gradient Flow as the limit of the Sinkhorn Algorithm
This limit, which we call the Sinkhorn flow, is an example of a Wasserstein mirror gradient flow, a concept we introduce here inspired by the well-known Euclidean mirror gradient flows.
Stochastic optimization on matrices and a graphon McKean-Vlasov limit
The limiting curve of graphons is characterized by a family of stochastic differential equations with reflections and can be thought of as an extension of the classical McKean-Vlasov limit for interacting diffusions.
Triangular Flows for Generative Modeling: Statistical Consistency, Smoothness Classes, and Fast Rates
Triangular flows, also known as Kn\"{o}the-Rosenblatt measure couplings, comprise an important building block of normalizing flow models for generative modeling and density estimation, including popular autoregressive flow models such as real-valued non-volume preserving transformation models (Real NVP).
Entropy Regularized Optimal Transport Independence Criterion
We introduce an independence criterion based on entropy regularized optimal transport.
Gradient flows on graphons: existence, convergence, continuity equations
Wasserstein gradient flows on probability measures have found a host of applications in various optimization problems.
Asymptotics of Discrete Schrödinger Bridges via Chaos Decomposition
We consider instead in this paper the problem where each matching is endowed with a Gibbs probability weight proportional to the exponential of the negative total cost of that matching.
Community detection in the sparse hypergraph stochastic block model
We consider the community detection problem in sparse random hypergraphs.
