A weekly online seminar on all aspects of Monte Carlo methods.
Join us weekly to explore recent developments and connect with a diverse community of passionate researchers in this exciting field!
Videos of past talks available on YouTube.
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Upcoming Seminar Presentations
All seminars are on Tuesdays [ 8:30 am PT ] = [ 11:30 am ET ] = [ 4:30 pm London ] = [ 5:30 pm Paris ] = [ 11:30 pm Beijing + 1d]
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Tuesday, November 4, 2025
Speaker: Filippo Ascolani (Duke University) [Zoom Link]
Title: Coordinate-wise MCMC schemes for structured high-dimensional Bayesian models
Abstract: Coordinate-wise MCMC schemes (e.g. Gibbs and Metropolis-within-Gibbs) are popular algorithms to sample from posterior distributions arising from Bayesian models. We discuss recent developments in the analysis of such algorithms in high-dimensional scenarios. In the first part of the talk we discuss hierarchical models with a large number of groups, obtaining dimension-free convergence results for coordinate-wise samplers under random data-generating assumptions, for a broad class of two-level models with generic likelihood function. Specific examples with Gaussian, binomial and categorical likelihoods are discussed. In the second part we show that if the target distribution is strongly log-concave then the random scan Gibbs sampler contracts in relative entropy and provide a sharp characterization of the associated contraction rate. Extension to Metropolis-within-Gibbs schemes and a comparison with gradient-based methods are also discussed. This is based on joint works with Giacomo Zanella (Bocconi University), Hugo Lavenant (Bocconi University) and Gareth Roberts (University of Warwick).
Tuesday, November 11, 2025
Speaker: Samuel Livingstone (University College London) [Zoom Link]
Title: New preconditioning theory and methodology for MCMC sampling algorithms
Abstract: I will discuss two pieces of work. In the first we present theoretical results related to preconditioning Markov chain Monte Carlo sampling algorithms. Preconditioning is a widely used technique that is known empirically to improve the mixing of Markov chain algorithms, but little has been said theoretically about it. I will present some recent work establishing some positive and negative theoretical results. I will then discuss some methodological work devising new preconditioners. The standard options to choose from in common software packages are 'diagonal' or 'dense'. We will present a new alternative option that seeks to improve upon diagonal preconditioning whilst also being less computationally expensive than the quadratic cost required for the dense option. Both projects are joint work with my former PhD student Max Hird, now a PDRA at University of Waterloo. The first is associated with this paper: https://www.jmlr.org/papers/v26/23-1633.html.
Tuesday, November 18, 2025
Speaker: Yixin Wang (University of Michigan) [Zoom Link]
Title: Reuniting Bayesian Inference and Generative Modeling: A Two-Way Integration
Abstract: Bayesian inference and generative modeling were historically intertwined, but modern developments have diverged into separate toolkits: Bayesian inference reasons about latent structures, while generative modeling emphasizes density estimation and high-fidelity sampling. This talk presents two recent works that reconnect these traditions in different directions, showing how each can strengthen the other.
In the first part, I introduce Posterior Mean Matching (PMM), which derives generative modeling directly from online Bayesian inference. PMM translates conjugate Bayesian updates into iterative refinement rules for sampling, yielding a family of expressive generative models for real-valued, count, and discrete data. This perspective reveals how classical Bayesian machinery can underlie state-of-the-art generative processes across diverse data modalities.
In the second part, I move in the opposite direction with Structured Flow Autoencoders (SFA), which use powerful generative flows to enhance Bayesian inference. SFA augments graphical models with conditional flow-based likelihoods and introduces a flow-matching objective that explicitly accounts for latent variables, enabling simultaneous learning of structured posteriors and high-quality generators. Applied to images, videos, and single-cell RNA-seq data, SFA delivers both stronger latent representations and higher quality samples.
Together, these works show that combining Bayesian inference’s structural understanding with generative modeling’s expressive sampling can produce models that are more interpretable, more adaptable across data types, and capable of producing high-quality generations.
This is joint work with Sebastian Salazar, Michal Kucer, Emily Casleton, David Blei, Yidan Xu, and Long Nguyen.
Tuesday, November 25, 2025
Speaker: Guan-Horng Liu (Meta AI) [Zoom Link]
Title: Sampling with Schrödinger Bridge—An Adjoint-Matching Perspective
Abstract: Computational methods for learning to sample from the Boltzmann distribution—where the target distribution is known only up to an unnormalized energy function—have advanced significantly recently. Due to the lack of explicit target samples, prior diffusion samplers often require importance-weighted estimation or complicated learning processes. Both trade off scalability with extensive evaluations of the energy and model, thereby limiting their practical usage. In this talk, I will introduce Adjoint Schrödinger Bridge Sampler (ASBS), a new diffusion sampler that employs simple and scalable matching-based objectives yet without the need to estimate target samples during training. ASBS is grounded on a mathematical model—the Schrödinger Bridge—which enhances sampling efficiency via kinetic-optimal transportation. Through a new lens of stochastic optimal control theory, we demonstrate how SB-based diffusion samplers can be learned at scale via Adjoint Matching and prove convergence to the global solution. We demonstrate the effectiveness of ASBS on sampling from classical energy functions, amortized conformer generation, and molecular Boltzmann distributions.
Tuesday, Dec 2, 2025
Speaker: Rocco Caprio (University of Warwick) [Zoom Link]
Format and Rules
The seminars are held on Zoom and last 60 minutes.
A moderator collects audience questions in Q&A section.
Moderators may ask you to unmute yourself to participate in the discussion. Please note that you may be recorded if you activate your audio or video during the seminar.
Organizers
Valentin De Bortoli (Google DeepMind)
Yang Chen (University of Michigan)
Nianqiao Ju (Dartmouth College)
Sifan Liu (Duke University)
Sam Power (University of Bristol)
Qian Qin (University of Minnesota)
Guanyang Wang (Rutgers University)
Feedback and Suggestions
If you have feedback or suggestions or want to propose a speaker, email us at internationalmontecarloseminar@gmail.com, or fill out this form.
Acknowledgements
Sincerely thank Ying Jin for the patient guidance
Gratefully inspired by the Online Causal Inference Seminar