Search Results for author:
Found 7 papers, 4 papers with code
Sequential transport maps using SoS density estimation and $α$-divergences
Transport-based density estimation methods are receiving growing interest because of their ability to efficiently generate samples from the approximated density.
Self-reinforced polynomial approximation methods for concentrated probability densities
We approximate the complicated target density by a composition of self-reinforced KR rearrangements, in which previously constructed KR rearrangements -- based on the same approximation ansatz -- are used to precondition the density approximation problem for building each new KR rearrangement.
Scalable conditional deep inverse Rosenblatt transports using tensor-trains and gradient-based dimension reduction
We present a novel offline-online method to mitigate the computational burden of the characterization of posterior random variables in statistical learning.
Learning non-Gaussian graphical models via Hessian scores and triangular transport
Undirected probabilistic graphical models represent the conditional dependencies, or Markov properties, of a collection of random variables.
Minimizing rational functions: a hierarchy of approximations via pushforward measures
This paper is concerned with minimizing a sum of rational functions over a compact set of high-dimension.
Optimization and Control
On the representation and learning of monotone triangular transport maps
Transportation of measure provides a versatile approach for modeling complex probability distributions, with applications in density estimation, Bayesian inference, generative modeling, and beyond.
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.
