Past Speakers

Many problems in science and engineering require the repeated simulation of complex physical systems. Applications such as uncertainty quantification, optimization, parameter estimation, and related many-query tasks can therefore become computationally prohibitive when relying solely on high-fidelity numerical simulations. Machine-learning surrogate models are designed to approximate such simulations at a significantly reduced computational cost while enabling the integration of observational and experimental data. In this talk, we discuss surrogate modeling approaches based on convolutional and graph neural networks, as well as operator-learning methods that approximate mappings between function spaces. Particular emphasis is placed on applications involving complex geometries, multiscale phenomena, high-dimensional parametrizations, and limited training data. We further discuss how physical knowledge can be incorporated into these models and combined with data, with applications in fluid dynamics, geoscience, Earth system modeling, and biomedical simulations.

The conditional backward sampling particle filter (CBPF; also known as the particle Gibbs with ancestor sampling, PGAS) is a powerful Markov chain Monte Carlo sampler for general state space hidden Markov model smoothing. It was proposed as an improvement over the conditional particle filter, which is known to have an O(T^2) computational time complexity under a general ‘strong mixing’ assumption of the model, where T is the time horizon. We provide the first proof that the CBPF admits an O(T log T) computational complexity under strong mixing, complementing strong empirical evidence of the superiority of the CBPF in practice. In particular, the CBPF’s mixing time is upper bounded by O(log T), for any sufficiently large number of particles N that depends only on the mixing constants and not T. We show that an O(log T) mixing time is optimal. The proof involves the analysis of a novel coupling of two CBPFs, which involves a maximal coupling of two particle systems at each time instant. The coupling is implementable, and thus can also be used to construct unbiased, finite variance, estimates of functionals which have arbitrary dependence on the latent state’s path, with a total expected cost of O(Tlog T). The talk is based on joint work with Joona Karjalainen, Sumeetpal S. Singh and Anthony Lee.

In this talk, we introduce a novel Bayesian nonparametric method for quantile estimation/regression based on the martingale posterior (MP) framework. The core idea of the MP is that posterior sampling is equivalent to predictive imputation, which allows us to break free of the stringent likelihood-prior specification. We demonstrate that a recursive estimate of a smooth quantile function, subject to a martingale condition, is entirely sufficient for full nonparametric Bayesian inference. We term the resulting posterior distribution as the quantile martingale posterior (QMP), which arises from an implicit generative predictive distribution. Associated with the QMP is an expedient, MCMC-free and parallelizable posterior computation scheme, which can be further accelerated with an asymptotic approximation based on a Gaussian process. Furthermore, the well-known issue of monotonicity in quantile estimation is naturally alleviated through increasing rearrangement due to the connections to the Bayesian bootstrap, and the QMP has a particularly tractable form that allows for comprehensive theoretical study.

I will discuss an approach for constrained Bayesian optimisation which uses ideas from PDE-constrained optimisation to pre-compute an approximation of the feasible subset of the optimisation domain. This approximation is injected as a prior into the traditional optimisation loop. I will also introduce and discuss its application to divertor optimisation in tokamak fusion reactors via the Grad-Shafranov equation. This is work in progress, in collaboration with the UK Atomic Energy Authority.

Gaussian random fields (GFs) are fundamental tools in spatial modeling and can be represented flexibly and efficiently as solutions to stochastic partial differential equations (SPDEs). The SPDEs depend on specific parameters, which govern various field behaviors and can be estimated using Bayesian inference. Informative priors are essential to ensure meaningful posterior covariance structures. This study builds on previous work by constructing penalized complexity (PC) priors for a smooth, invertible parameterization of the correlation range, diffusion matrix, and variance of a non-stationary GF. The formulated prior is weakly informative, effectively penalizing complexity by pushing the model towards stationarity while allowing for enough flexibility to capture non-stationary behavior. The model is applied to model precipitation in Spain, particulate matter in California, and electoral data in France with promising results.

Embedding probability distributions into reproducing kernel Hilbert spaces (RKHS) has enabled powerful non-parametric methods such as the maximum mean discrepancy (MMD), a statistical distance with strong theoretical and computational properties. At its core, the MMD relies on kernel mean embeddings (KMEs) to represent distributions as mean functions in RKHS . However, it remains unclear if the mean function is the only meaningful RKHS representation. Inspired by generalised quantiles, we introduce the notion of kernel quantile embeddings (KQEs), along with a consistent estimator. We then use KQEs to construct a family of distances that (i) are probability metrics under weaker kernel conditions than MMD ;(ii) recover a kernelised form of the sliced Wasserstein distance; and(iii) can be efficiently estimated with near-linear cost. Through hypothesis testing, we show that these distances offer a competitive alternative to MMD and its fast approximations. Our findings demonstrate the value of representing distributions in Hilbert space beyond simple mean functions, paving the way for new avenues of research.

Generative models are able to create new and diverse samples, yet the mechanisms that let them generalize rather than memorize remain unclear. This talk presents recent theoretical insights into how autoencoders, adversarial models, and diffusion models learn structure from data while controlling model capacity and preventing overfitting. By drawing on ideas from information theory, statistical learning, and training dynamics, the talk offers a clear picture of why these models can generalize effectively and why some of them show a strong natural resistance to memorization.

The talk is mainly based on my previous work [3] and will cover introduction to other closely related works [1, 2, 4].

Gaussian processes (GPs) have gained popularity as flexible machine learning models for regression and function approximation with an in-built method for uncertainty quantification. GPs suffer when the amount of training data is large or when the underlying function contains multiscale features that are difficult to represent by an isotropic kernel. The training of GPs with large scale data is often performed through inducing point approximations (also known as sparse GP regression), where the size of the covariance matrices in GP regression is reduced considerably through a greedy search on the data set. Deep Gaussian processes have recently been introduced as hierarchical models that resolve multi-scale features by composing multiple Gaussian processes. Whilst GPs can be trained through a simple analytical formula, deep GPs require a sampling or, more usual, a variational approximation. Variational approximations lead to large-scale stochastic, non-convex optimisation problems and the resulting approximation tends to represent uncertainty incorrectly. In this work, we combine variational learning with MCMC to develop a particle-based expectation-maximisation method to simultaneously find inducing points within the large-scale data (variationally) and accurately train the Gaussian processes (sampling-based). The result is a highly efficient and accurate methodology for deep GP training on large scale data. We test the method on standard benchmark problems. Joint work with Aretha Teckentrup and Simon Urbainczyk.

Count-compositional data arise in many different fields, including high-throughput microbiome sequencing and palynology experiments, where a common, important goal is to understand how covariates relate to the observed compositions. Existing methods often fail to simultaneously address key challenges inherent in such data, namely: overdispersion, an excess of zeros, cross-sample heterogeneity, and nonlinear covariate effects. In this talk, we first present novel probabilistic portrayals of two multivariate models designed to handle zero-inflation in count-compositional data. Then, to address the above concerns, we propose novel Bayesian nonparametric models based on ensembles of regression trees. Specifically, we leverage the recently introduced zero-and-N-inflated multinomial distribution and assign independent nonparametric Bayesian additive regression tree (BART) priors to both the compositional and structural zero probability components of our model, to flexibly capture covariate effects. We further extend this by adding latent random effects to capture overdispersion and more general dependence structures among the categories. We develop an efficient inferential algorithm combining recent data augmentation schemes with established BART sampling routines. We evaluate our proposed models in simulation studies and illustrate their applicability with two case studies in microbiome and palaeoclimate modelling.

Doubly intractable problems occur when both the likelihood and the posterior are available only in unnormalised form, with computationally intractable normalisation constants. Bayesian inference then typically requires direct approximation of the posterior through specialised and typically expensive MCMC methods. In this paper, we provide a computationally efficient alternative in the form of a novel generalised Bayesian posterior that allows for conjugate inference within the class of exponential family models for discrete data. We derive theoretical guarantees to characterise the asymptotic behaviour of the generalised posterior, supporting its use for inference. The method is evaluated on a range of challenging intractable exponential family models, including the Conway-Maxwell-Poisson graphical model of multivariate count data, autoregressive discrete time series models, and Markov random fields such as the Ising and Potts models. The computational gains are significant; in our experiments, the method is between 10 and 6000 times faster than state-of-the-art Bayesian computational methods.

I will discuss the mechanism of denoising diffusion models (DDMs) for sampling from multimodal distributions on $\mathbb{R}^d$. The first part of the talk will review the basics of DDMs — from discrete Markov chains to continuous-time formulations via SDEs. Then, using a mixture of two Gaussians as a canonical example of a multimodal target, I will describe how DDMs gradually transform the initial prior (a standard Gaussian) into the bimodal target distribution. In particular, I will show analytically that denoising trajectories dynamically change their behaviour during sampling, and that the denoising procedure can be characterised roughly by three stages: 1. Early stage — Contraction; 2. Intermediate stage — Expansion (contraction is lost); 3. Final stage — local attraction to a single mode, possibly contracting again locally. I will also clarify how these stages depend on properties of the target distribution, such as dimension, separation between modes, and the variances of the mixture components. This talk is based on ongoing joint work with Paul Fearnhead.

We propose function-space versions of autoencoders—machine-learning methods for dimension reduction and generative modelling—in both their deterministic (FAE) and variational (FVAE) forms. Formulating autoencoder objectives in function space enables training and evaluation with data discretised at arbitrary resolutions, leading to new applications such as inpainting, superresolution, and generative modelling. We discuss the technical challenges of formulating autoencoders in infinite dimension. A key issue is that FVAE’s variational inference is often ill defined, unlike in finite dimensions, limiting its applicability. We then explore specific problem classes where FVAE remains useful. We contrast this with the FAE objective, which remains well defined in many situations where FVAE fails, making it a robust and versatile alternative. We demonstrate both methods on scientific data sets, including Navier–Stokes fluid flow simulations. This is joint work with Justin Bunker and Mark Girolami (Cambridge), Andrew M. Stuart (Caltech) and T. J. Sullivan (Warwick).

Several emerging post-Bayesian methods target a probability distribution for which an entropy-regularised variational objective is minimised. This increased flexibility introduces a computational challenge, as one loses access to an explicit unnormalised density for the target. To mitigate this difficulty, we introduce a novel measure of suboptimality called ‘gradient discrepancy’, and in particular a ‘kernel’ gradient discrepancy (KGD) that can be explicitly computed. In the standard Bayesian context, KGD coincides with the kernel Stein discrepancy (KSD), and we obtain a novel characterisation of KSD as measuring the size of a variational gradient. Outside this familiar setting, KGD enables novel sampling algorithms to be developed and compared, even when unnormalised densities cannot be obtained. To illustrate this point several novel algorithms are proposed and studied, including a natural generalisation of Stein variational gradient descent, with applications to mean-field neural networks and predictively oriented posteriors presented. On the theoretical side, our principal contribution is to establish sufficient conditions for desirable properties of KGD, such as continuity and convergence control.

We introduce a framework of time-changed Markov processes to speed up the convergence of Markov chain Monte Carlo (MCMC) algorithms in the context of multimodal distributions and rare event simulation. The time-changed process is defined by adjusting the speed of time of a base process via a user-chosen, state-dependent function. We apply this framework to several Markov processes from the MCMC literature, such as Langevin diffusions and piecewise deterministic Markov processes, obtaining novel modifications of classical algorithms and also re-discovering known MCMC algorithms. We prove theoretical properties of the time-changed process under suitable conditions on the base process, focusing on connecting the stationary distributions and qualitative convergence properties such as geometric and uniform ergodicity, as well as a functional central limit theorem. Time permitting, we will compare our approach with the framework of space transformations, clarifying the similarities between the approaches. This is joint work with Andrea Bertazzi.

Inference in stochastic reaction-network models—such as the SEIR epidemic model or the Lotka–Volterra predator–prey system—is crucial for understanding the dynamics of interacting systems in epidemiology, ecology, and systems biology. These models are typically represented as Markov jump processes (MJPs) with intractable likelihoods. As a result, particle Markov chain Monte Carlo (particle MCMC) methods, particularly the Particle Gibbs (PG) sampler, have become standard tools for Bayesian inference. However, PG suffers from severe particle degeneracy, especially in high-dimensional state spaces, leading to poor mixing and inefficiency. In this talk, I focus on improving the efficiency of particle MCMC methods for inference in reaction networks by addressing the degeneracy problem. Building on recent work on the Exchangeable Particle Gibbs (xPG) sampler for continuous-state diffusions, this project develops a novel version of xPG tailored to discrete-state reaction networks, where randomness is driven by Poisson processes rather than Brownian motion. The proposed method retains the exchangeability framework of xPG while adapting it to the structural and computational challenges specific to reaction networks.

We analyse the tension between robustness and efficiency for Markov chain Monte Carlo (MCMC) sampling algorithms. In particular, we focus on robustness of MCMC algorithms with respect to heterogeneity in the target and their sensitivity to tuning, an issue of great practical relevance but still understudied theoretically. We show that the spectral gap of the Markov chains induced by classical gradient-based MCMC schemes (e.g. Langevin and Hamiltonian Monte Carlo) decays exponentially fast in the degree of mismatch between the scales of the proposal and target distributions, while for the random walk Metropolis (RWM) the decay is linear.

Stochastic block model (SBM) is a popular choice for clustering nodes in a network. In this talk, a few versions of SBM will be reviewed, with the focus on the clustering approach (hard vs soft), and its relation with the subsequent MCMC algorithm. Model selection and some practical issues will also be discussed.

Likelihood-based inference and its variants provide a statistically efficient and widely applicable approach to parametric inference, their application to models involving intractable likelihoods poses challenges. In this work, we study a class of minimum distance estimators for intractable generative models, that is, statistical models for which the likelihood is intractable, but simulation is cheap. The distance considered, maximum mean discrepancy (MMD), is defined through the embedding of probability measures into a reproducing kernel Hilbert space.

In this talk, I introduce a generalized representation of Bayesian inference. It is derived axiomatically, recovering existing Bayesian methods as special cases. It is then used to prove that variational inference (VI) based on the Kullback-Leibler Divergence with a variational family Q produces the optimal Q-constrained approximation to the exact Bayesian inference problem. Surprisingly, this implies that standard VI dominates any other Q-constrained approximation to the exact Bayesian inference problem. This means that alternative Q-constrained approximations such as VI minimizing other divergences and Expectation Propagation can produce better posteriors than VI only by implicitly targeting more appropriate Bayesian inference problems.

I will describe some applications of Gaussian process models to single-cell data. We have developed a scalable implementation of the Gaussian process latent variable model (GPLVM) that can be used for pseudotime estimation when there is prior knowledge about pseudotime, e.g. from capture times available in single-cell time course data [1]. Other dimensions of the GPLVM latent space can then be used to model additional sources of variation, e.g. from branching of cells into different lineages.

Sampling from multimodal target distributions is a classical challenging problem. Markov Chain Monte Carlo methods typically rely on localised or gradient based proposal mechanisms and so target distributions exhibiting multimodality mean the chain becomes trapped in a local mode and this results in a bias sample output. This talk introduces a novel algorithm, ALPS, that is designed to provide a scalable approach to sampling from multimodal target distributions. The ALPS algorithm concatenates a number of the strengths of the current gold standard approaches for multimodality.

We are interested in inferring the phylogeny, or shared ancestry, of a set of species descended from a common ancestor. When traits pass vertically through ancestral relationships, the phylogeny is a tree and one can often compute the likelihood efficiently through recursions. Lateral transfer, whereby evolving species exchange traits outside of ancestral relationships, is a frequent source of model misspecification in phylogenetic inference. We propose a novel model of species diversification which explicitly controls for the effect of lateral transfer.

Probabilistic and Bayesian reasoning is one of the principle theoretical pillars to our understanding of machine learning. Over the last two decades, it has inspired a whole range of successful machine learning methods and influenced the thinking of many researchers in the community. On the other hand, in the last few years the rise of deep learning has completely transformed the field and led to a string of phenomenal, era-defining, successes.