Search Results for author: Adrien Corenflos

Found 11 papers, 11 papers with code

Conditioning diffusion models by explicit forward-backward bridging

1 code implementation22 May 2024 Adrien Corenflos, Zheng Zhao, Simo Särkkä, Jens Sjölund, Thomas B. Schön

Given an unconditional diffusion model $\pi(x, y)$, using it to perform conditional simulation $\pi(x \mid y)$ is still largely an open question and is typically achieved by learning conditional drifts to the denoising SDE after the fact.

Denoising

Nesting Particle Filters for Experimental Design in Dynamical Systems

1 code implementation12 Feb 2024 Sahel Iqbal, Adrien Corenflos, Simo Särkkä, Hany Abdulsamad

In this paper, we propose a novel approach to Bayesian experimental design for non-exchangeable data that formulates it as risk-sensitive policy optimization.

Experimental Design

Particle-MALA and Particle-mGRAD: Gradient-based MCMC methods for high-dimensional state-space models

1 code implementation26 Jan 2024 Adrien Corenflos, Axel Finke

In experiments, for both highly and weakly informative prior dynamics, our methods substantially improve upon both CSMC and sophisticated 'classical' MCMC approaches.

Bayesian Inference

Risk-Sensitive Stochastic Optimal Control as Rao-Blackwellized Markovian Score Climbing

1 code implementation21 Dec 2023 Hany Abdulsamad, Sahel Iqbal, Adrien Corenflos, Simo Särkkä

Stochastic optimal control of dynamical systems is a crucial challenge in sequential decision-making.

Decision Making

Parallel-in-Time Probabilistic Numerical ODE Solvers

1 code implementation2 Oct 2023 Nathanael Bosch, Adrien Corenflos, Fatemeh Yaghoobi, Filip Tronarp, Philipp Hennig, Simo Särkkä

Probabilistic numerical solvers for ordinary differential equations (ODEs) treat the numerical simulation of dynamical systems as problems of Bayesian state estimation.

Variational Gaussian filtering via Wasserstein gradient flows

1 code implementation11 Mar 2023 Adrien Corenflos, Hany Abdulsamad

We present a novel approach to approximate Gaussian and mixture-of-Gaussians filtering.

Auxiliary MCMC and particle Gibbs samplers for parallelisable inference in latent dynamical systems

1 code implementation1 Mar 2023 Adrien Corenflos, Simo Särkkä

We introduce two new classes of exact Markov chain Monte Carlo (MCMC) samplers for inference in latent dynamical models.

De-Sequentialized Monte Carlo: a parallel-in-time particle smoother

1 code implementation4 Feb 2022 Adrien Corenflos, Nicolas Chopin, Simo Särkkä

We propose dSMC (de-Sequentialized Monte Carlo), a new particle smoother that is able to process $T$ observations in $\mathcal{O}(\log T)$ time on parallel architecture.

Temporal Gaussian Process Regression in Logarithmic Time

1 code implementation19 Feb 2021 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.

regression

Differentiable Particle Filtering via Entropy-Regularized Optimal Transport

1 code implementation15 Feb 2021 Adrien Corenflos, James Thornton, George Deligiannidis, Arnaud Doucet

Particle Filtering (PF) methods are an established class of procedures for performing inference in non-linear state-space models.

Variational Inference

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