• Ba, Y., de Wiljes, J., Oliver, D.S., and Reich, S. (2021). Randomized maximum likelihood based posterior sampling, Computational Geosciences, https://doi.org/10.1007/s10596-021-10100-y arXiv:2101.03612

  • Pathiraja, S., Reich, S., Stannat, W. (2021): McKean-Vlasov SDEs in nonlinear filtering. SIAM Journal on Control and Optimization.  doi:10.1137/20M1355197 arXiv 2007.12658

  • Gottwald, G.A., and Reich, S. (2021). Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observations Chaos: An Interdisciplinary Journal of Nonlinear Science, Vol. 31, 101103, doi:10.1063/5.0066080 arXiv:2108.03561

  • Pidstrigach, J. and Reich, S. (2021). Affine-invariant ensemble transform methods for logistic regression arXiv: 2104.08061

  • Pathiraja, S. and Stannat, W. (2021). Analysis of the feedback particle filter with diffusion map based approximation of the gain. Foundations of Data Science. doi:10.3934/fods.2021023 arXiv:2109.02761

  • Schindler, D., Moldenhawer, T., Stange, M., Lepro, V., Beta, C., Holschneider, M., and Huisinga, W. (2021). Analysis of protrusion dynamics in amoeboid cell motility by means of regularized contour flows. PLoS Comput Biol 17(8): e1009268. doi:journal.pcbi.1009268

  • Geßner, H. (2021). Transparently Safeguarding Good Research Data Management with the Lean Process Assessment Model. In: E-Science-Tage 2021: Share Your Research Data. Heidelberg. DOI: 10.11588/heidok.00029719

  • Dietrich, F., Makeev, A., Kevrekidis, G., Evangelou, N., Bertalan, T., Reich, S., and Kevrekidis, I.G. (2021). Learning effective stochastic differential equations from microscopic simulations: combining stochastic numerics and deep learning. arXiv:2106.09004

  • Rabe, M. M., Chandra, J., Krügel, A., Seelig, S. A., Vasishth, S., & Engbert, R. (2021). A Bayesian approach to dynamical modeling of eye-movement control in reading of normal, mirrored, and scrambled texts. Psychological Review doi:10.1037/rev0000268, psyarXiv

  • Pasemann, G. and Flemming, S. and Alonso, S. and Beta, C. and Stannat, W. (2020). Diffusivity Estimation for Activator-Inhibitor Models: Theory and Application to Intracellular Dynamics of the Actin Cytoskeleton. Journal of Nonlinear Science 31, 59 (2021). doi:10.1007/s00332-021-09714-4  arXiv 2005.09421

  • Gottwald, G., and Reich, S. (2021). Supervised learning from noisy observations: Combining machine-learning techniques with data assimilation. Physica D, Vol. 423, 132911. doi:10.1016/j.physd.2021.132911. arXiv:2007.07383

  • Wormell, C.L. and Reich, S. (2021). Spectral convergence of diffusion maps: Improved error bounds and an alternative normalisation. SIAM Journal Numerical Analysis,59, 1687-1734. arXiv 2006.02037; doi:10.1137/30M1344093

  • Hartung, N., Wahl, M., Rastogi, A., and Huisinga, W. (2021). Nonparametric goodness-of-fit tests for parametric covariate models in pharmacometric analyses. CPT Pharmacometrics & Systems Pharmacology 10: 564-576. ArXiv 2011.07539 DOI

  • Cialenco, I. and Kim, H.-J. and Pasemann, G. (2021). Statistical analysis of discretely sampled semilinear SPDEs: a power variation approach. arXiv:2103.04211

  • Blanchard, G., Deshmukh, A., Dogan, U., Lee, G. and Scott, C. (2021). Domain Generalization by Marginal Transfer Learning. Journal of Machine Learning Research 22(2):1−55. Open Access

  • Zadorozhnyi, O. and Gaillard, P. and Gerchinovitz, S. and Rudi, A. (2021). Online nonparametric regression with Sobolev kernels. arxiv: 2102.03594

  • Lange, T. and Stannat W. (2021). Mean field limit of Ensemble Square Root filters - discrete and continuous time, Foundations of Data Science. doi: 10.3934/fods.2021003

  • Reich, S. and Weissmann, S. (2021). Fokker-Planck particle systems for Bayesian inference: Computational approaches, SIAM/ASA J. Uncertainty Quantification, 9(2), 446–482. doi: 10.1137/19M1303162arXiv:1911.10832

  • Ba, Y., de Wiljes, J., Oliver, D.S., and Reich, S. (2021). Randomized maximum likelihood based posterior samplingarXiv:2101.03612