Search Results for author:
Found 16 papers, 10 papers with code
Computational Hypergraph Discovery, a Gaussian Process framework for connecting the dots
Type 1: Approximate an unknown function given input/output data.
Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference
PCP-Map models conditional transport maps as the gradient of a partially input convex neural network (PICNN) and uses a novel numerical implementation to increase computational efficiency compared to state-of-the-art alternatives.
An adaptive ensemble filter for heavy-tailed distributions: tuning-free inflation and localization
In these settings, the Kalman filter and its ensemble version - the ensemble Kalman filter (EnKF) - that have been designed under Gaussian assumptions result in degraded performance.
A generative flow for conditional sampling via optimal transport
Sampling conditional distributions is a fundamental task for Bayesian inference and density estimation.
Score-based Diffusion Models in Function Space
They consist of a forward process that perturbs input data with Gaussian white noise and a reverse process that learns a score function to generate samples by denoising.
Ensemble transport smoothing. Part II: Nonlinear updates
Smoothing is a specialized form of Bayesian inference for state-space models that characterizes the posterior distribution of a collection of states given an associated sequence of observations.
Ensemble transport smoothing. Part I: Unified framework
A companion paper (Ramgraber et al., 2023) explores the implementation of nonlinear ensemble transport smoothers in greater depth.
Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transport
We tackle this challenging Bayesian inference problem using a likelihood-free approach based on measure transport together with the construction of summary statistics for the image data.
A low-rank ensemble Kalman filter for elliptic observations
We propose a regularization method for ensemble Kalman filtering (EnKF) with elliptic observation operators.
Diagonal Nonlinear Transformations Preserve Structure in Covariance and Precision Matrices
For a multivariate normal distribution, the sparsity of the covariance and precision matrices encodes complete information about independence and conditional independence properties.
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.
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.
Conditional Sampling with Monotone GANs: from Generative Models to Likelihood-Free Inference
We present a novel framework for conditional sampling of probability measures, using block triangular transport maps.
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.
Bayesian Optimization of Combinatorial Structures
The optimization of expensive-to-evaluate black-box functions over combinatorial structures is an ubiquitous task in machine learning, engineering and the natural sciences.
Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting
We present an algorithm to identify sparse dependence structure in continuous and non-Gaussian probability distributions, given a corresponding set of data.
