1. Data assimilation - the collaborative research centre SFB1294
  2. Open Science
  3. Publications

Publications

  • Carpentier, A., Rouyer, C., Tsybakov, A., and Akhavan, A. (2026): Sharp Optimal Algorithm for Derivative-Free Stochastic Convex Optimization in One Dimension. arXiv: 2607.12938 [math.OC]. URL:
    arxiv.org/abs/2607.12938

  • König, J., Cheng Lie, H., (2026). Posterior error bounds for prior-driven balancing in linear Gaussian inverse problems, arXiv 2601.03971

  • Jiang, Z, Andreou, M, Reich, S, Chen, N (2026) A Continuous-Time Ensemble Kalman-Bucy Smoother for Causal Inference and Model Discovery arXiv preprint arXiv:2604.25157

  • Abedi, E., Bechtold, F., Rehmeier, M (2026): Non-uniqueness of nonlinear Markov processes in the sense of McKean associated with parabolic PDEs, https://doi.org/10.48550/arXiv.2604.25851

  • Josie König, Elizabeth Qian, Melina A. Freitag (2025). Dimension and model reduction approaches for linear Bayesian inverse problems with rank-deficient prior covariances, arXiv 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 generation arXiv:2505.04417

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

  • 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

  • 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

  • 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

     

  • 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

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

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

  • 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

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

  • 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

  • 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

  • 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

  • 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