Stein Variational Gradient Descent with Matrix-Valued Kernels

The collection of papers about combining deep learning and Bayesian nonparametrics

Paper List For Linking ODE and Deep Learning

Code for "Function Space Particle Optimization for Bayesian Neural Networks"

Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019

Deep Gaussian Processes with Importance-Weighted Variational Inference

MATLAB code for Stein Point Markov Chain Monte Carlo.

Codes for "Understanding MCMC Dynamics as Flows on the Wasserstein Space" (ICML-19)

Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)

a repo sharing Bayesian Neural Network recent papers

A list of variational inference algorithms and their performance on MNIST

Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

The collection of recent papers about variational inference

code for Stein Neural Sampler

Seminars DeepBayes Summer School 2018

Natural Gradient, Variational Inference

Python and MATLAB code for Stein Variational sampling methods

Code for "A Spectral Approach to Gradient Estimation for Implicit Distributions" (ICML'18)

The code for Meta Learning for SGMCMC

Lagrangian VAE

Papers for Bayesian-NN

Final version of the submitted thesis, for my PhD degree

Code for "A-NICE-MC: Adversarial Training for MCMC"

A variational inference method with accurate uncertainty estimation. It uses a new semi-implicit variational family built on neural networks and hierarchical distribution (ICML 2018).

code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"