Publications
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.
Yadav, H., Smith, G., Reich, S., and Vasishth, S. (2022). Number feature distortion modulates cue-based retrieval in reading. doi:10.31234/osf.io/s4c9t.
Gaudlitz, S. and Reiß, M. (2022). Estimation for the reaction term in semi-linear SPDEs under small diffusivity. arXiv:2203.10527
Reich, S. (2022). Frequentist perspective on robust parameter estimation using the ensemble Kalman filter arXiv:2201.000611
Pidstrigach, J. and Reich, S. (2021). Affine-invariant ensemble transform methods for logistic regression arXiv: 2104.08061
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
Cialenco, I. and Kim, H.-J. and Pasemann, G. (2021). Statistical analysis of discretely sampled semilinear SPDEs: a power variation approach. arXiv:2103.04211
Zadorozhnyi, O. and Gaillard, P. and Gerchinovitz, S. and Rudi, A. (2021). Online nonparametric regression with Sobolev kernels. arxiv: 2102.03594
Ba, Y., de Wiljes, J., Oliver, D.S., and Reich, S. (2021). Randomized maximum likelihood based posterior sampling. arXiv:2101.03612
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.
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
Altmeyer, R. and Bretschneider, T. and Janák, J. and Reiß, M. (2020). Parameter Estimation in an SPDE Model for Cell Repolarisation. arXiv 2010.06340
Houdebert, P., Zass, A. (2020), An explicit continuum Dobrushin uniqueness criterion for Gibbs point processes with non-negative pair potentials. arxiv 2009.06352
Molkenthin, C., Donner, C., Reich, S., Zöller, G., Hainzl, S., Holschneider, M. and Opper, M. (2020). GP-ETAS: Semiparametric Bayesian inference for the spatio-temporal Epidemic Type Aftershock Sequence model. arXiv:2005.12857
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
Altmeyer, R. and Cialenco, I. and Pasemann, G. (2020). Parameter estimation for semilinear SPDEs from local measurements. arXiv 2004.14728
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