9 papers
Wasserstein Contraction of Coordinate Ascent Variational Inference
Rocco Caprio, Adrien Corenflos, Sam Power
We study the non-asymptotic contraction in Wasserstein distance of the sequential, parallel, and random-scan coordinate ascent variational inference algorithms. This is shown to ho…
A coupling-based approach to f-divergences diagnostics for Markov chain Monte Carlo
Adrien Corenflos, Hai-Dang Dau
A long-standing gap exists between the theoretical analysis of Markov chain Monte Carlo convergence, which is often based on statistical divergences, and the diagnostics used in pr…
Maximin Robust Bayesian Experimental Design
Hany Abdulsamad, Sahel Iqbal, Christian A. Naesseth +2
We address the brittleness of Bayesian experimental design under model misspecification by formulating the problem as a max--min game between the experimenter and an adversarial na…
Robust Automatic Differentiation of Square-Root Kalman Filters via Gramian Differentials
Adrien Corenflos
Square-root Kalman filters propagate state covariances in Cholesky-factor form for numerical stability, and are a natural target for gradient-based parameter learning in state-spac…
Particle Gibbs without the Gibbs bit
Adrien Corenflos
Exact parameter and trajectory inference in state-space models is typically achieved by one of two methods: particle marginal Metropolis-Hastings (PMMH) or particle Gibbs (PGibbs).…
Modelling pathwise uncertainty of Stochastic Differential Equations samplers via Probabilistic Numerics
Yvann Le Fay, Simo Särkkä, Adrien Corenflos
Probabilistic ordinary differential equation (ODE) solvers have been introduced over the past decade as uncertainty-aware numerical integrators. They typically proceed by assuming…