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Found 4 papers, 2 papers with code
Coupling techniques for nonlinear ensemble filtering
We consider filtering in high-dimensional non-Gaussian state-space models with intractable transition kernels, nonlinear and possibly chaotic dynamics, and sparse observations in space and time.
Greedy inference with structure-exploiting lazy maps
We prove weak convergence of the generated sequence of distributions to the posterior, and we demonstrate the benefits of the framework on challenging inference problems in machine learning and differential equations, using inverse autoregressive flows and polynomial maps as examples of the underlying density estimators.
A Stein variational Newton method
Stein variational gradient descent (SVGD) was recently proposed as a general purpose nonparametric variational inference algorithm [Liu & Wang, NIPS 2016]: it minimizes the Kullback-Leibler divergence between the target distribution and its approximation by implementing a form of functional gradient descent on a reproducing kernel Hilbert space.
Inference via low-dimensional couplings
In the context of statistics and machine learning, these transformations can be used to couple a tractable "reference" measure (e. g., a standard Gaussian) with a target measure of interest.
