6 papers · 1 filter
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…
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…
Conditioning diffusion models by explicit forward-backward bridging
Adrien Corenflos, Zheng Zhao, Simo Särkkä +2
Given an unconditional diffusion model targeting a joint model , using it to perform conditional simulation is still largely an open question and is typica…
Recursive Nested Filtering for Efficient Amortized Bayesian Experimental Design
Sahel Iqbal, Hany Abdulsamad, Sara Pérez-Vieites +2
This paper introduces the Inside-Out Nested Particle Filter (IO-NPF), a novel, fully recursive, algorithm for amortized sequential Bayesian experimental design in the non-exchangea…
Nesting Particle Filters for Experimental Design in Dynamical Systems
Sahel Iqbal, Adrien Corenflos, Simo Särkkä +1
In this paper, we propose a novel approach to Bayesian experimental design for non-exchangeable data that formulates it as risk-sensitive policy optimization. We develop the Inside…