11 citations · 30 across the 21 of their papers we have counts for
22 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…
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…
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…
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…
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).…
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…
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 typicall…
BlackJAX: Composable Bayesian inference in JAX
Alberto Cabezas, Adrien Corenflos, Junpeng Lao +19
BlackJAX is a library implementing sampling and variational inference algorithms commonly used in Bayesian computation. It is designed for ease of use, speed, and modularity by tak…
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…
Particle-MALA and Particle-mGRAD: Gradient-based MCMC methods for high-dimensional state-space models
Adrien Corenflos, Axel Finke
State-of-the-art methods for Bayesian inference in state-space models are (a) conditional sequential Monte Carlo (CSMC) algorithms; (b) sophisticated 'classical' MCMC algorithms li…
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…
Risk-Sensitive Stochastic Optimal Control as Rao-Blackwellized Markovian Score Climbing
Hany Abdulsamad, Sahel Iqbal, Adrien Corenflos +1
Stochastic optimal control of dynamical systems is a crucial challenge in sequential decision-making. Recently, control-as-inference approaches have had considerable success, provi…
Parallel-in-Time Probabilistic Numerical ODE Solvers
Nathanael Bosch, Adrien Corenflos, Fatemeh Yaghoobi +3
Probabilistic numerical solvers for ordinary differential equations (ODEs) treat the numerical simulation of dynamical systems as problems of Bayesian state estimation. Aside from…
Debiasing Piecewise Deterministic Markov Process samplers using couplings
Adrien Corenflos, Matthew Sutton, Nicolas Chopin
Monte Carlo methods -- such as Markov chain Monte Carlo (MCMC) and piecewise deterministic Markov process (PDMP) samplers -- provide asymptotically exact estimators of expectations…
Variational Gaussian filtering via Wasserstein gradient flows
Adrien Corenflos, Hany Abdulsamad
We present a novel approach to approximate Gaussian and mixture-of-Gaussians filtering. Our method relies on a variational approximation via a gradient-flow representation. The gra…
Auxiliary MCMC and particle Gibbs samplers for parallelisable inference in latent dynamical systems
Adrien Corenflos, Simo Särkkä
Sampling from the full posterior distribution of high-dimensional non-linear, non-Gaussian latent dynamical models presents significant computational challenges. While Particle Gib…
Parallel square-root statistical linear regression for inference in nonlinear state space models
Fatemeh Yaghoobi, Adrien Corenflos, Sakira Hassan +1
In this article, we introduce parallel-in-time methods for state and parameter estimation in general nonlinear non-Gaussian state-space models using the statistical linear regressi…
De-Sequentialized Monte Carlo: a parallel-in-time particle smoother
Adrien Corenflos, Nicolas Chopin, Simo Särkkä
Particle smoothers are SMC (Sequential Monte Carlo) algorithms designed to approximate the joint distribution of the states given observations from a state-space model. We propose…
The Coupled Rejection Sampler
Adrien Corenflos, Simo Särkkä
We propose a coupled rejection-sampling method for sampling from couplings of arbitrary distributions. The method relies on accepting or rejecting coupled samples coming from domin…
Temporal Gaussian Process Regression in Logarithmic Time
Adrien Corenflos, Zheng Zhao, Simo Särkkä
The aim of this article is to present a novel parallelization method for temporal Gaussian process (GP) regression problems. The method allows for solving GP regression problems in…
Differentiable Particle Filtering via Entropy-Regularized Optimal Transport
Adrien Corenflos, James Thornton, George Deligiannidis +1
Particle Filtering (PF) methods are an established class of procedures for performing inference in non-linear state-space models. Resampling is a key ingredient of PF, necessary to…
Parallel Iterated Extended and Sigma-point Kalman Smoothers
Fatemeh Yaghoobi, Adrien Corenflos, Sakira Hassan +1
The problem of Bayesian filtering and smoothing in nonlinear models with additive noise is an active area of research. Classical Taylor series as well as more recent sigma-point ba…