Papers
Discussed papers before 1st January 2023. For up-to-date papers, please refer to the links provided on the Talks webpage.
2022
- December 15th: Coupling MCMC algorithms in high dimensions [arXiv]
- November 24th: Explicit convergence bounds for Metropolis Markov chains: isoperimetry, spectral gaps and profiles [arXiv]
- November 3rd: Manifold Markov chain Monte Carlo methods for Bayesian inference in diffusion models [arXiv]
- October 27th: The Barker proposal: Combining robustness and efficiency in gradient-based MCMC [JRSSB]
- October 20th: Martingale posterior distributions [arXiv]
- October 6th: Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods [arXiv]
- September 29th: Comparison of Markov chains via weak Poincaré inequalities with application to pseudo-marginal MCMC [arXiv]
- September 22nd: Inference in Stochastic Epidemic Models via Multinomial Approximations [AISTAT2021]
- September 15th: Metropolis–Hastings via Classification [JASA]
- June 23rd: Non-Reversible Parallel Tempering: a Scalable Highly Parallel MCMC Scheme [arXiv]
- June 9th: Continuously-Tempered PDMP samplers [arXiv]
- May 26th: Scalable Importance Tempering and Bayesian Variable Selection [arXiv]
- May 5th: Compressed Empirical Measures (in finite dimensions) [arXiv]
- March 31st: Parameter Estimation for the McKean-Vlasov Stochastic Differential Equation [arXiv]
- March 24th: Elliptical slice sampling [arXiv]
- February 24th: Boost your favorite MCMC sampler using Kac’s theorem: the Kick-Kac teleportation algorithm [arXiv]
- February 17th: Metropolis Adjusted Underdamped Langevin Trajectories: a robust alternative to Hamiltonian Monte-Carlo [arXiv]
- February 3rd: Statistical scalability and approximate inference in distributed computing environments [arXiv]
- Januray 27th: The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables [arXiv]
- January 20th: Non-reversible guided Metropolis kernel [arXiv]
- January 13th: The Apogee to Apogee Path Sampler [arXiv]
2021
- December 9th: Metropolis-Hastings with Averaged Acceptance Ratios [arXiv]
- December 2nd: Waste-free sequential Monte Carlo [araXiv]
- November 25th: Double Control Variates for Gradient Estimation in Discrete Latent Variable Models [arXiv]
- November 4th: Approximations of Piecewise Deterministic Markov Processes and their convergence properties [arXiv]
- October 28th: Multilevel Linear Models, Gibbs Samplers and Multigrid Decompositions [euclid]
- June 22nd: Bayesian computation from 1763 to the 21st Century [arXiv]
- June 10th: Sequential Monte Carlo algorithms for agent-based models of disease transmission [arXiv]
- May 27th: Harold Jeffreys’s Theory of Probability Revisited [arXiv]
- May 13th: Dynamic Bayesian Neural Networks [arXiv]
- April 19th: Inference from Iterative Simulation Using Multiple Sequences [euclid]
- April 19th: Rank-Normalization, Folding, and Localization: An Improved R for Assessing Convergence of MCMC [euclid]
- March 4th Differentiable Particle Filtering via Entropy-Regularized Optimal Transport [arXiv]
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]