Parallel Iterated Extended and Sigma-Point Kalman Smoothers. - Fatemeh Yaghoobi; Adrien Corenflos; Sakira Hassan; Simo Särkkä (ICASSP, 2021)

Differentiable Particle Filtering via Entropy-Regularized Optimal Transport. - Adrien Corenflos; James Thornton; George Deligiannidis; Arnaud Doucet (ICML, 2021)

Gaussian Process Regression in Logarithmic Time. - Adrien Corenflos; Zheng Zhao 004; Simo Särkkä (CoRR, 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; et al. (J. Mach. Learn. Res., 2021)

Temporal Gaussian Process Regression in Logarithmic Time. - Adrien Corenflos; Zheng Zhao 004; Simo Särkkä (FUSION, 2022)

De-Sequentialized Monte Carlo: a parallel-in-time particle smoother. - Adrien Corenflos; Nicolas Chopin; Simo Särkkä (J. Mach. Learn. Res., 2022)

Variational Gaussian Filtering via Wasserstein Gradient Flows. - Adrien Corenflos; Hany Abdulsamad (EUSIPCO, 2023)

Auxiliary MCMC and particle Gibbs samplers for parallelisable inference in latent dynamical systems. - Adrien Corenflos; Simo Särkkä (CoRR, 2023)

Debiasing Piecewise Deterministic Markov Process samplers using couplings. - Adrien Corenflos; Matthew Sutton; Nicolas Chopin (CoRR, 2023)

Risk-Sensitive Stochastic Optimal Control as Rao-Blackwellized Markovian Score Climbing. - Hany Abdulsamad; Sahel Iqbal; Adrien Corenflos; Simo Särkkä (CoRR, 2023)

Nesting Particle Filters for Experimental Design in Dynamical Systems. - Sahel Iqbal; Adrien Corenflos; Simo Särkkä; Hany Abdulsamad (ICML, 2024)

Modelling pathwise uncertainty of Stochastic Differential Equations samplers via Probabilistic Numerics. - Yvann Le Fay; Simo Särkkä; Adrien Corenflos (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; et al. (CoRR, 2024)

Recursive Nested Filtering for Efficient Amortized Bayesian Experimental Design. - Sahel Iqbal; Hany Abdulsamad; Sara Pérez-Vieites; Simo Särkkä; Adrien Corenflos (CoRR, 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)

Computationally efficient statistical inference in Markovian models. - Adrien Corenflos (2024)

Conditioning diffusion models by explicit forward-backward bridging. - Adrien Corenflos; Zheng Zhao 004; Thomas B. Schön; Simo Särkkä; Jens Sjölund (AISTATS, 2025)

Parallel Square-Root Statistical Linear Regression for Inference in Nonlinear State Space Models. - Fatemeh Yaghoobi; Adrien Corenflos; Sakira Hassan; Simo Särkkä (SIAM J. Sci. Comput., 2025)