Bayesian Machine Learning

1. General Machine Learning

1. Simplified PAC-Bayesian Margin Bounds
2. Latent Dirichlet Allocation
3. Relational Dependency Networks

2. MCMC

1. MCMC using Hamiltonian dynamics
2. A Complete Recipe for Stochastic Gradient MCMC
3. Riemann Manifold Langevin and Hamiltonian Monte Carlo
4. A-NICE-MC: Adversarial Training for MCMC
5. NICE: Non-linear Independent Components Estimation
6. Markov Chain Monte Carlo and Variational Inference: Bridging the Gap

3. Variational Autoencoder

1. Auto-Encoding Variational Bayes
2. Tutorial on Variational Autoencoders

4. Variational Inference

1. Black Box Variational Inference
2. Variational Inference - A Review for Statisticians
3. Variational Inference with Normalizing Flow
4. MADE- Masked Autoencoder for Distribution Estimation
5. Masked Autoregressive Flow for Density Estimation
6. Neural Autoregressive Distribution Estimation