### Papers

# 2020

#### Stein's method

- October 22nd:
*The Boomerang Sampler*[arXiv] - October 8th: Coullon and Webber
*Ensemble sampler for infinite-dimensional inverse problems*[arXiv] - September 23rd:
*Discontinuous Hamiltonian Monte Carlo for discrete parameters and discontinuous likelihoods*[arXiv] - September 3rd:
*Involutive MCMC: a Unifying Framework*[arXiv] - July 23rd: Hoffman and Ma
*Black-Box Variational Inference as Distilled Langevin Dynamics*[ICML] - June 11th: Neal
*Non-reversibly updating a uniform [0,1] value for Metropolis accept/reject decisions*[arXiv] - March 12th: Pu et al.
*VAE Learning via Stein Variational Gradient Descent*[arXiv] - February 2nd: Liu and Wang
*Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm*[arXiv] - January 30th: Nemeth
*Introduction to Stein's method*

# 2019

#### Continuous Time MCMC

- December 19th: Wang et. al.
*Regeneration-enriched Markov processes with application to Monte Carlo*[arXiv] - December 12th:
*Extending the Zig Zag Sampler to general velocity distributions* - November 28th:
*Introduction to Piece-wise Deterministic Markov Processes* - November 14th: Bierkens
*Non-reversible Metropolis-Hastings*[Paper]

#### Reparameterisation

- October 31st: Maddison et. al.
*The Concrete Distribution*[arXiv] - October 17th: Kingma & Welling
*Auto-Encoding Variational Bayes*[arXiv]

#### Optimal Transport

- October 3rd Srivastava et. al.
*Scalable Bayes via Barycenter in Wasserstein Space*[JMLR Paper] - September 13th: Peyre & Cuturi
*Computational Optimal Transport: Chapter 5*[arXiv] - August 29th: Bernton et. al.
*On parameter estimation with the Wasserstein distance*[arXiv] - August 22nd: Parno et. al.
*Transport map accelerated Markov chain Monte Carlo*[arXiv] - August 8th:
*Introduction to the Wasserstein distance*[Resources] - July 4th: Jacob et. al.:
*Unbiased Markov chain Monte Carlo with couplings*[Link]

#### Recommender Systems

- May 2nd: Salakhutdinov and Mnih:
*Bayesian Probabilistic Matrix Factorizationusing Markov Chain Monte Carlo*[Link] - April 11th: Nilesh et. al.
*Magnetic Hamiltonian Monte Carlo*[arXiv]

#### Big Data

- March 21st: Zhang et. al.
*Determinantal Point Processes for Mini-Batch Diversification*[arXiv] - March 13th: Hensman et. al.
*Fast Nonparametric Clustering of Structured Time-Series*[arXiv]

#### Gaussian Processes

- March 7th: Durrande et. al.
*Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era*[arXiv] - February 7th: Finley et. al.
*Efficient algorithms for Bayesian Nearest Neighbor Gaussian Processes*[arXiv]

#### Optimisation

- January 24th: Ma et. al.
*Sampling Can Be Faster Than Optimization*[arXiv] - January 17th: Bubeck et. al.
*A geometric alternative to Nesterov's accelerated gradient descent*[arXiv]

# 2018

- November 8th: Qiang Liu
*Stein Variational Gradient Descent as Gradient Flow*[arXiv] - November 1st: Oren Mangoubi et. al.
*Does Hamiltonian Monte Carlo mix faster than a random walk on multimodal densities?*[arXiv] - January 18th: Marzouk et al
*An introduction to sampling via measure transport*[arXiv]

# 2017

- November 16th: Goncalves et al
*Barker's algorithm for Bayesian inference with intractable likelihoods*[arXiv] - October 26th: Polson et al
*Deep Learning: A Bayesian Perspective*[arXiv] - June 1st: Nishimura et al -
*Discontinuous Hamiltonian Monte Carlo for sampling discrete parameters*[arXiv] - May 11th: Graham and Storkey -
*Continuously tempered Hamiltonian Monte Carlo*[arXiv] - April 27th: Pakman et al.-
*Stochastic bouncy particle sampler*[arXiv] - March 29th: Duan et al.
*Data augmentation for scalable Markov chain Monte Carlo*[arXiv] - March 22nd: Lin and Dunson
*Bayesian monotone regression using Gaussian process projection*[arXiv] - March 9th: Gomez-Rubio and Rue
*Markov chain Monte Carlo with the integrated nested Laplace approximation*[arXiv] - Janurary 19th: Murray et al.
*Anytime Monte Carlo*[arXiv]

# 2016

- October 26th: Chkrebtii et al.
*Bayesian Solution Uncertainty Quantification for Differential Equations*[Link]