Publications

S. Beier, V. Pfeifer, A. Datta, R. Großmann, C. Beta (2025). Deciphering the dual chemotaxis strategy of bacteria in porous mediaarXiv:2503.05286

Josie König, Elizabeth Qian, Melina A. Freitag (2025). Dimension and model reduction approaches for linear Bayesian inverse problems with rank-deficient prior covariancesarXiv 2506.23892

Opper, Manfred & Reich, S. (2025). On a mean-field Pontryagin minimum principle for stochastic optimal controlarXiv:2506.10506

Reich, S. (2025) Ensemble Kalman-Bucy filtering for nonlinear model predictive controlarXiv:2503.12474

Gottwald, G.A., Liu, S., Marzouk, Y., Reich, S. & Tong, X.T. (2025) Localized diffusion models for high dimensional distributions generationarXiv:2505.04417

Gottwald, Georg A. & Reich, S. (2024). Localized Schrödinger bridge sampler arXiv:2409.07968

Albrecht, J., Opper, M., and Großmann, R. (2024): Inferring Parameter Distributions in Heterogeneous Motile Particle Ensembles: A Likelihood Approach for Second Order Langevin Models. arxiv:2411.08692

Datta, A., Beier, S., Pfeifer, V., Großmann, R., and Beta, C. (2024): Bacterial motility in porous media follows an active renewal process with power-law distributed dwell times. arxiv: 2408.02317

Carere, G. and Lie, H. C. (2024). Generalised rank-constrained approximations of Hilbert-Schmidt operators on separable Hilbert spaces and applications, arXiv 2408.05104

Carere, G. and Lie, H. C. (2024). Optimal low-rank approximations of posteriors for linear Gaussian inverse problems on Hilbert spaces, arXiv 2411.01112

Spokoiny, V. (2024). Estimation for SLS models: finite sample guarantees, arXiv:2404.14227

Lie, H. C. (2024): Bayesian inference of covariate-parameter relationships for population modelling. ArXiv 2407.09640

Tiepner, A. and Ziebell, E. (2024): Parameter estimation in hyperbolic linear SPDEs from multiple measurements. arXiv:2407.13461

Ziebell, E. (2024): Non-parametric estimation for the stochastic wave equation. arXiv:2404.18823

Albrecht, J. and Reich, S. (2024): Robust parameter estimation for partially observed second-order diffusion processes. arXiv:2406.14738

Siobhán Correnty, Melina A. Freitag, Kirk M. Soodhalter (2023). Chebyshev HOPGD with sparse grid sampling for parameterized linear systems. arXiv:2309.14178

Kim, J. W. and Mehta, P. G. (2024): Arrow of Time in Estimation and Control: Duality Theory Beyond the Linear Gaussian Model. arXiv 2405.07650

Kim, J. W., Taghvaei, A., and Mehta, P. G. (2024): Divergence metrics in the study of Markov and hidden Markov processes. arXiv 2404.15779

Cherepanov, V., and Ertel, S. W. (2024): Neural Networks-based Random Vortex Methods for Modelling Incompressible Flows. arXiv: 2405.13691

Tienstra, M. (2024). Early Stopping for Ensemble Kalman-Bucy Inversion. arXiv:2403.18353

Gottwald, G., Li, F., Marzouk, Y., Reich, S (2024). Stable generative modelling using diffusion maps. arXiv 2401.04372

Engbert, R. and Rabe, M. M. (2023). Tutorial on dynamical modeling of eye movements in reading. doi: 10.31234/osf.io/dsvmt

Lopopolo, A. and Rabovsky, M. (2023). Tracking lexical and semantic prediction error underlying the N400 using artificial neural network models of sentence processing. doi: 10.1101/2022.11.14.516396

Bhandari, D., Pidstrigach, J., and Reich, S. (2023). Affine Invariant Ensemble Transform Methods to Improve Predictive Uncertainty in ReLU Networks. arXiv:2309.04742 

Spokoiny, V. (2023). Deviation bounds for the norm of a random vector under exponential moment conditions with applications, arXiv:2309.02302

Reich, S. (2023): A particle-based Algorithm for Stochastic Optimal Control. arXiv 2311.06906

Spokoiny, V. (2023). Sharp deviation bounds and concentration phenomenon for the squared norm of a sub-Gaussian vector, arXiv:2305.07885v1

Pasemann, G., Beta C. and Stannat, W. (2023). Stochastic Reaction-Diffusion Systems in Biophysics: Towards a Toolbox for Quantitative Model Evaluation. arXiv: 2307.06655

Spokoiny, V. (2023). Nonlinear regression: finite sample guarantees, arXiv:2305.08193

Spokoiny, V. (2023). Mixed Laplace approximation for marginal posterior and Bayesian inference in error-in-operator model, arXiv:2305.09336

Chen, Y, Huang D.Z., Huang J., Reich, S., and Stuart, A.M. (2023). Sampling via gradient flows in the space of probability measures. arXiv:2310.03597

Pidstrigach, J., Marzouk, Y., Reich, S., and Wang, S. (2023). Infinite-Dimensional Diffusion Models. arXiv 2302.10130

Liu, S., Reich, S., and Tong, X.T. (2023). Dropout ensemble Kalman inversion for high dimensional inverse problems. arXiv:2308.16784

Reiß, M., Strauch, C., and Trottner, L. (2023): Change point estimation for a stochastic heat equation. arXiv:2307.10960

Pasemann, G., Beta, C., and Stannat, W. (2023): Stochastic Reaction-Diffusion Systems in Biophysics: Towards a Toolbox for Quantitative Model Evaluation. arXiv:2307.06655

Gaudlitz, S. (2023): Non-parametric estimation of the reaction term in semi-linear SPDEs with spatial ergodicity.arXiv:2307.05457

Kim, J. W. and Mehta, P. G. (2023). Variance Decay Property for Filter Stability. arXiv 2305.12850

Chen, Y, Huang D.Z., Huang J., Reich, S., and Stuart, A.M. (2023). Gradient flows for sampling: Mean-field models, Gaussian approximations and affine invariance. arXiv:2302.11024

Cvetkovic, N., Lie, H. C., Bansal, H., and Veroy-Grepl, K. (2023): Choosing observation operators to mitigate model error in Bayesian inverse problems. ArXiv 2301.04863

Kim, J.W. and Reich, S. (2023): On forward-backward SDE approaches to continuousßtime minimum variance estimation. arXiv 2304.12727

Pidstrigach, J., Marzouk, Y., Reich, S., and Wang., S. (2023). Infinite-dimensional diffusion models for function spaces arXiv:2302.10130

Irwin, B. and Reich, S. (2023). EnKSGD: A class of preconditioned black box optimization and inversion algorithms. arXiv:2303.16494.

Schwetlick, L. and Reich S. and Engbert R. (2023). Bayesian Dynamical Modeling of Fixational Eye Movements. arXiv:2303.11941.

Rabe, M. M., Paape, D., Mertzen, D., Vasishth, S., and Engbert, R. (2023). SEAM: An integrated activation-coupled model of sentence processing and eye movements in reading. arXiv:2303.05221

Janák, J. and Reiß, M. (2023): Parameter estimation for the stochastic heat equation with multiplicative noise from local measurements. arXiv:2303.00074v1

Kemeth, F., Alonso, S., Echebarria, B., Moldenhawer, T., Beta, C. and Kevrekidis I. (2022). Black and Gray Box Learning of Amplitude Equations: Application to Phase Field Systems. arXiv: 2207.03954

Schindler, D., Moldenhawer, T., Beta, C., Huisinga, W. and Holschneider, M. (2022). Three-component contour dynamics model to simulate and analyze amoeboid cell motility. arXiv:2210.12978

Reich, S. (2022): Data assimilation: A dynamic homotopy-based coupling approach. arXiv 2209.05279

Huang, D.Z., Huang, J., Reich, S., and Stuart, A.M. (2022). Efficient derivative-free Bayesian inference for large-scale inverse problems. arXiv:2204.04386.

Gaudlitz, S. and Reiß, M. (2022): Estimation for the reaction term in semi-linear SPDEs under small diffusivity. arXiv:2203.10527

Manegueu, A. G., Carpentier, A., & Yu, Y. (2021). Generalized non-stationary bandits. arXiv preprint arXiv:2102.00725

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

Coghi, M., Torstein, N., Nuesken, N., and Reich, S. (2022). Rough McKean-Vlasov dynamics for robust ensemble Kalman filtering arXiv:2107.06621

Lange, T. (2020): Derivation of Ensemble Kalman-Bucy Filters with unbounded nonlinear coefficients. arXiv 2012.07572

Pathiraja, S. (2020): L2 convergence of smooth approximations of Stochastic Differential Equations with unbounded coefficients. arXiv 2011.13009

Castillo, A. M., de Wiljes, J., Shprits, Y. Y., and Aseev, N. A. (2020). Reconstructing the dynamics of the outerelectron radiation belt by means of the standard and ensemble Kalman filter with the VERB-3Dcode, ESSOAr. doi:10.1002/essoar.10504674.

Carpentier, A., Vernade, C., and Abbasi-Yadkori, Y. (2020): The elliptical potential lemma revisited. arXiv: 2010.10182.

Seelig, S., Risse, S., and Engbert, R. (2020). Predictive modeling of the influence of parafoveal informationprocessing on eye guidance in reading.  doi:10.31234/osf.io/vbmqn

Holschneider, M., Ferrat, K., Zöller, G., Molkenthin, C., and Hainzl, S. (2020). Richter b-value maps from local moments of seismicity.  arXiv:2010.12298

Houdebert, P., Zass, A. (2020), An explicit continuum Dobrushin uniqueness criterion for Gibbs point processes with non-negative pair potentials. arxiv 2009.06352

Rastogi, A. and Mathé, P. (2020): Inverse learning in Hilbert scales.arXiv 2002.10208

Celisse, A. and Wahl, M. (2020): Analyzing the discrepancy principle for kernelized spectral filter learning algorithms.arXiv: 2004.08436

Maier C., Hartung N., Kloft C., Huisinga W., de Wiljes J. (2020): Combining reinforcement learning with data assimilation for individualised dosing policies in oncology. arXiv:2006.01061

Zhelavskaya, I., Aseev, N. A., Shprits, Y. Y., and Spasojevi, M. (2020). A combined neural network- and physics-based approach for modeling the plasmasphere dynamics, ESSOAr. doi:10.1002/essoar.10502691.1

Duval, C. and Mariucci, E. (2020): Non-asymptotic control of the cumulative distribution function of Lévy processes. arXiv 2003.09281

Vernade, C., Carpentier, A., Lattimore, T., Zappella, G., Ermis, B. and Brueckner, M. (2020): Linear Bandits with Stochastic Delayed Feedback. arXiv:1807.02089

Spokoiny, V. (2019). Bayesian inference for nonlinear inverse problems. arXiv:1912.12694

Lange, T. and Stannat, W. (2019): On the continuous time limit of Ensemble Square Root Filters. arXiv 1910.12493

Spokoiny, V., and Panov, M. (2019). Accuracy of Gaussian approximation in nonparametric Bernstein–vonMises theorem. arXiv:1910.06028

Nuesken, N. and Reich, S. (2019). Note on Interacting Langevin diffusions: Gradient structure and ensemble Kalman sampler by Garbuno-Inigo, Hoffmann, Li and Stuart. arXiv:1908.10890

Houdebert, P. (2019). Phase transition of the non-symmetric Continuum Potts model. arXiv: 1908.10066

Avanesov, V. (2019). How to gamble with non-stationary X-armed bandits and have no regrets. arXiv:1908.07636

Avanesov, V. (2019). Structural break analysis in high-dimensional covariance structure. arXiv: 1803.00508

Avanesov, V. (2019). Nonparametric Change Point Detection in Regression. arXiv:1903.02603

Lefakis, L., Zadorozhnyi, O. and Blanchard, G. (2019): Efficient Regularized Piecewise-Linear Regression Trees. arXiv: 1907.00275 

Zadorozhnyi, O., Blanchard, G., and Carpentier, A. (2019): Restless dependent bandits with fading memory. arXiv: 1906.10454 

Blanchard, G., Mathé, P., and Mücke, N. (2019): Lepskii Principle in Supervised Learning. arXiv: 1905.10764 

Wahl, M. (2019): A note on the prediction error of principal component regression.arXiv: 1811.02998

Carpentier, A., Duval, C., and Mariucci, E. (2019): Total variation distance for discretely observed Lévy processes: a Gaussian approximation of the small jumps. arXiv: 1810.02998

Duval, C. and Mariucci, E. (2019): Compound Poisson approximation to estimate the Lévy density. arXiv: 1702.08787 

Jirak, M. and Wahl, M. (2018): Perturbation bounds for eigenspaces under a relative gap condition.arXiv: 1803.03868

Pathiraja, S. and van Leeuwen, P.J. (2018). Model uncertainty estimation in data assimilation for multi-scale systems with partially observed resolved variables, Quarterly Journal of the Royal Meteorological Society, under review, arXiv: 1807.09621

Jirak, M. and Wahl, M. (2018): Relative perturbation bounds with applications to empirical covariance operators.arXiv: 1802.02869

Gribonval, R., Blanchard, G.,  Keriven, N. and Traonmilin, Y. (2017). Compressive Statistical Learning with Random Feature Moments.arXiv 1706.07180