Adrien Corenflos

According to our database, Adrien Corenflos authored at least 18 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Computationally efficient statistical inference in Markovian models.

Adrien Corenflos

PhD thesis, 2024

Parallel-in-Time Probabilistic Numerical ODE Solvers.

Nathanael Bosch, Adrien Corenflos, Fatemeh Yaghoobi, Filip Tronarp, Philipp Hennig, Simo Särkkä

J. Mach. Learn. Res., 2024

Recursive Nested Filtering for Efficient Amortized Bayesian Experimental Design.

Sahel Iqbal, Hany Abdulsamad, Sara Pérez-Vieites, Simo Särkkä, Adrien Corenflos

CoRR, 2024

Conditioning diffusion models by explicit forward-backward bridging.

Adrien Corenflos, Zheng Zhao, Simo Särkkä, Jens Sjölund, Thomas B. Schön

CoRR, 2024

BlackJAX: Composable Bayesian inference in JAX.

Alberto Cabezas, Adrien Corenflos, Junpeng Lao, Rémi Louf, Antoine Carnec, Kaustubh Chaudhari, Reuben Cohn-Gordon, Jeremie Coullon, Wei Deng, Sam Duffield, Gerardo Duràn-Martín, Marcin Elantkowski, Dan Foreman-Mackey, Michele Gregori, Carlos Iguaran, Ravin Kumar, Martin Lysy, Kevin Murphy, Juan Camilo Orduz, Karm Patel, Xi Wang, Rob Zinkov

CoRR, 2024

Modelling pathwise uncertainty of Stochastic Differential Equations samplers via Probabilistic Numerics.

Yvann Le Fay, Simo Särkkä, Adrien Corenflos

CoRR, 2024

Nesting Particle Filters for Experimental Design in Dynamical Systems.

Sahel Iqbal, Adrien Corenflos, Simo Särkkä, Hany Abdulsamad

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

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

Hany Abdulsamad, Sahel Iqbal, Adrien Corenflos, Simo Särkkä

CoRR, 2023

Debiasing Piecewise Deterministic Markov Process samplers using couplings.

Adrien Corenflos, Matthew Sutton, Nicolas Chopin

CoRR, 2023

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

Adrien Corenflos, Simo Särkkä

CoRR, 2023

Variational Gaussian Filtering via Wasserstein Gradient Flows.

Adrien Corenflos, Hany Abdulsamad

Proceedings of the 31st European Signal Processing Conference, 2023

2022

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

Adrien Corenflos, Nicolas Chopin, Simo Särkkä

J. Mach. Learn. Res., 2022

Parallel square-root statistical linear regression for inference in nonlinear state space models.

Fatemeh Yaghoobi, Adrien Corenflos, Sakira Hassan, Simo Särkkä

CoRR, 2022

Temporal Gaussian Process Regression in Logarithmic Time.

Adrien Corenflos, Zheng Zhao, Simo Särkkä

Proceedings of the 25th International Conference on Information Fusion, 2022

2021

POT: Python Optimal Transport.

Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T. H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, Titouan Vayer

J. Mach. Learn. Res., 2021

Gaussian Process Regression in Logarithmic Time.

Adrien Corenflos, Zheng Zhao, Simo Särkkä

CoRR, 2021

Differentiable Particle Filtering via Entropy-Regularized Optimal Transport.

Adrien Corenflos, James Thornton, George Deligiannidis, Arnaud Doucet

Proceedings of the 38th International Conference on Machine Learning, 2021

Parallel Iterated Extended and Sigma-Point Kalman Smoothers.

Fatemeh Yaghoobi, Adrien Corenflos, Sakira Hassan, Simo Särkkä

Proceedings of the IEEE International Conference on Acoustics, 2021