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Found 5 papers, 4 papers with code
Stochastic Localization via Iterative Posterior Sampling
Building upon score-based learning, new interest in stochastic localization techniques has recently emerged.
Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters
In this paper, we consider an entropic version of mOT with a tree-structured quadratic cost, i. e., a function that can be written as a sum of pairwise cost functions between the nodes of a tree.
Non-asymptotic convergence bounds for Sinkhorn iterates and their gradients: a coupling approach
Our approach is novel in that it is purely probabilistic and relies on coupling by reflection techniques for controlled diffusions on the torus.
Unbiased constrained sampling with Self-Concordant Barrier Hamiltonian Monte Carlo
In this paper, we propose Barrier Hamiltonian Monte Carlo (BHMC), a version of the HMC algorithm which aims at sampling from a Gibbs distribution $\pi$ on a manifold $\mathrm{M}$, endowed with a Hessian metric $\mathfrak{g}$ derived from a self-concordant barrier.
Differentially Private Federated Learning on Heterogeneous Data
Federated Learning (FL) is a paradigm for large-scale distributed learning which faces two key challenges: (i) efficient training from highly heterogeneous user data, and (ii) protecting the privacy of participating users.
