| Pot: Python optimal transport R Flamary, N Courty, A Gramfort, MZ Alaya, A Boisbunon, S Chambon, ... Journal of Machine Learning Research 22 (78), 1-8, 2021 | 972 | 2021 |
| Differentiable Particle Filtering via Entropy-Regularized Optimal Transport A Corenflos, J Thornton, G Deligiannidis, A Doucet International Conference on Machine Learning 139, 2100-2111, 2021 | 95 | 2021 |
| BlackJAX: Composable Bayesian inference in JAX A Cabezas, A Corenflos, J Lao, R Louf arXiv preprint arXiv:2402.10797, 2024 | 14 | 2024 |
| Parallel Iterated Extended and Sigma-point Kalman Smoothers F Yaghoobi, A Corenflos, S Hassan, S Särkkä ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 14 | 2021 |
| De-Sequentialized Monte Carlo: a parallel-in-time particle smoother A Corenflos, N Chopin, S Särkkä Journal of Machine Learning Research 23, 2022 | 9 | 2022 |
| Parallel square-root statistical linear regression for inference in nonlinear state space models F Yaghoobi, A Corenflos, S Hassan, S Särkkä SIAM Journal on Scientific Computing, 2025 | 7 | 2025 |
| The Coupled Rejection Sampler A Corenflos, S Särkkä arXiv preprint arXiv:2201.09585, 2022 | 6 | 2022 |
| Temporal Gaussian Process Regression in Logarithmic Time A Corenflos, Z Zhao, S Särkkä 2022 25th International Conference on Information Fusion (FUSION), 1-5, 2022 | 5 | 2022 |
| Conditioning diffusion models by explicit forward-backward bridging A Corenflos, Z Zhao, S Särkkä, J Sjölund, TB Schön arXiv preprint arXiv:2405.13794, 2024 | 4 | 2024 |
| Parallel-in-Time Probabilistic Numerical ODE Solvers N Bosch, A Corenflos, F Yaghoobi, F Tronarp, P Hennig, S Särkkä Journal of Machine Learning Research 25 (206), 1-27, 2024 | 4 | 2024 |
| Debiasing Piecewise Deterministic Markov Process samplers using couplings A Corenflos, M Sutton, N Chopin arXiv preprint arXiv:2306.15422, 2023 | 4 | 2023 |
| Nesting Particle Filters for Experimental Design in Dynamical Systems S Iqbal, A Corenflos, S Särkkä, H Abdulsamad International Conference on Machine Learning 139, 2100-2111, 2024 | 3 | 2024 |
| Risk-Sensitive Stochastic Optimal Control as Rao-Blackwellized Markovian Score Climbing H Abdulsamad, S Iqbal, A Corenflos, S Särkkä arXiv preprint arXiv:2312.14000, 2023 | 2 | 2023 |
| Particle-MALA and Particle-mGRAD: Gradient-based MCMC methods for high-dimensional state-space models A Corenflos, A Finke arXiv preprint arXiv:2401.14868, 2024 | 1 | 2024 |
| Auxiliary MCMC and particle Gibbs samplers for parallelisable inference in latent dynamical systems A Corenflos, S Särkkä arXiv preprint arXiv:2303.00301, 2023 | 1 | 2023 |
| Recursive Nested Filtering for Efficient Amortized Bayesian Experimental Design S Iqbal, H Abdulsamad, S Pérez-Vieites, S Särkkä, A Corenflos NeurIPS BDU Workshop, 2024 | | 2024 |
| Computationally efficient statistical inference in Markovian models A Corenflos Aalto University, 2024 | | 2024 |
| Modelling pathwise uncertainty of Stochastic Differential Equations samplers via Probabilistic Numerics YL Fay, S Särkkä, A Corenflos arXiv preprint arXiv:2401.03338, 2023 | | 2023 |
| Variational Gaussian filtering via Wasserstein gradient flows A Corenflos, H Abdulsamad 31st European Signal Processing Conference (EUSIPCO), 1838-1842, 2023 | | 2023 |
Simo SärkkäProfessor, Aalto UniversityBestätigte E-Mail-Adresse bei aalto.fi
