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

Publications

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

  • Schwetlick, L., Rothkegel, L.O.M., Trukenbrod, H.A., Engbert, R. (2020). Modeling the effects of perisaccadic attention on gaze statistics during scene viewing. Communications Biology, 3, 727. doi: 10.1038/s42003-020-01429-8

  • Moreno, E., Flemming, S., Font, F., Holschneider, M., Beta, C., and Alonso, S. (2020). Modeling cell crawlingstrategies with a bistable model: From amoeboid to fan-shaped cell motion. Physica D, 412:132591, doi:10.1016/j.physd.2020.132591

  • 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.

  • Makowski, S., Jäger, L. A., Prasse, P., & Scheffer, T. (2020). Biometric identification and presentation-attack detection using micro- and macro-movements of the eyes. International Joint Conference on Biometrics (IJCB), in press. Preprint: [1]

  • Prasse, P., Jäger, L. A., Makowski, S., Feuerpfeil, M., & Scheffer, T. (2020): On the Relationship between Eye Tracking Resolution and Performance of Oculomotoric Biometric Identification. Procedia Computer Science, 176, 2088-2097. doi: 10.1016/j.procs.2020.09.245

  • Makowski, S., Jäger, L. A., Schwetlick, L., Trukenbrod, H., Engbert, R., & Scheffer, T. (2020): Discriminative Viewer Identification using Generative Models of Eye Gaze. Procedia Computer Science, 176, 1348-1357. doi: 10.1016/j.procs.2020.09.144

  • Lange, T. and Stannat, W. (2020): On the continuous time limit of the Ensemble Kalman Filter. Mathematics of Computation, 40(327), 233-265. arXiv 1901.05204v1; doi:10.1090/mcom/3588

  • 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

  • Saggioro, E., de Wiljes, J., Kretschmer, M., and Runge, J. (2020). Reconstructing regime-dependent causalrelationships from observational time series. Chaos, 30(11):113115–1–113115–22, doi:10.1063/5.0020538

  • Engbert, R. (2021). Dynamical Models in Neurocognitive Psychology. Computational Approaches to Cognitionand Perception. Springer Nature. (in press)

  • 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 Reiß, M. (2020): Nonparametric estimation for linear SPDEs from local measurements. Annals of Applied Probability, to appear. arXiv 1903.06984

  • M. Stange, T. Moldenhawer, and C. Beta (2020): Fluorescent (C)LSM image sequences of dictyostelium discoideum (Ax2-LifeAct mRFP) for cell track and cell contour analysis. doi:10.5061/dryad. b5mkkwhbd.

  • Gaidzik, F., Pathiraja, S., Saalfeld, S., Stucht, D., Speck, O., Thevenin, D., Janiga, G. (2020). Hemodynamic Data Assimilation in a Subject-specific Circle of Willis Geometry. Clinical Neuroradiology, doi:10.1007/s00062-020-00959-2

  • Reich, S., and Rozdeba, P. J. (2020): Posterior contraction rates for non-parametric state and drift estimation. Foundation of Data Science, Vol. 2, 333-349. doi:10.3934/fods.2020016. arXiv:2003.09219

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

  • Malem-Shinitski, N., Opper, M., Reich, S., Schwetlick, S., Seelig S. A., and Engbert, R (2020): A Mathematical Model of Exploration and Exploitation in Natural Scene Viewing. PLoS Computational Biology. doi:10.1371/journal.pcbi.1007880

  • Rastogi, A. (2020): Tikhonov regularization with oversmoothing penalty for nonlinear statistical inverse problems. Communications on Pure & Applied Analysis 19(8): 4111-4126. ArXiv 2002.01303DOI

  • Rastogi, A., Blanchard, G. and Mathé, P. (2020): Convergence analysis of Tikhonov regularization for non-linear statistical inverse learning problems. Electronic Journal of Statistics 14(2): 2798-2841. ArXiv 1902.05404v2DO

  • Milbradt, C. and Wahl, M. (2020). High-probability bounds for the reconstruction error of PCA. Statist. Probab. Lett. 161. ArXiv 1909.10787

  • Reiß, M. and Wahl, M. (2020). Non-asymptotic upper bounds for the reconstruction error of PCA. Ann. Stat. 48(2): 1098-1123. arXiv 1609.03779

  • Rastogi, A. (2019). Tikhonov regularization with oversmoothing penalty for linear statistical inverse learning problems. AIP Conference Proceedings 2183(1): 110004 AIP Publishing LLC. DOI

  • 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

  • D. Schindler, T. Moldenhawer, L. Lindenmeier, and M. Holschneider. Amoepy (version 1.0), 2020. doi:10.5281/zenodo.3982372

  • Cervantes, S., Shprits, Y. Y., Aseev, N. A., and Allison, H. J. (2020). Quantifying the effects of EMIC wavescattering and magnetopause shadowing in the outer electron radiation belt by means of data as-similation. J. Geophys. Res.-Space, 125(8):e2020JA028208, doi:10.1029/2020JA028208

  • de Wiljes, J. and Tong, X. T (2020): Analysis of a localised nonlinear Ensemble Kalman Bucy Filter with complete and accurate observations. Nonlinearity, 33(9): 4752-4782 [2]  arXiv:1908.10580v3

  • Maoutsa, D., Reich, S., and Opper, M. (2020). Interacting particle solutions of Fokker-Planck equations through gradient-log-density estimation. Entropy, Vol. 22, 0802. doi:10.3390/e22080802. arXiv:2006.00702

  • Avanesov, V. (2020): Data-driven confidence bands for distributed nonparametric regression. Proceedings of Machine Learning Research vol. 125. DSpace,  PMLR

  • Zass, A. (2020): A Gibbs point process of diffusions: existence and uniqueness. Proceedings of the XI international conference stochastic and analytic methods in mathematical physics (Lectures in pure and applied mathematics 6), Universitätsverlag Potsdam, 13-22. Open Access

  • Houdebert, P. (2020): Numerical study for thephase transition of thearea-interaction model. Proceedings of the XI international conference stochastic and analytic methods in mathematical physics (Lectures in pure and applied mathematics 6), Universitätsverlag Potsdam, 165–174. Open Access

  • J. Cheshire, P. Menard, and A. Carpentier. The influence of shape constraints on the thresholding bandit problem. In: Conference on Learning Theory. PMLR. 2020, pp. 1228–1275, 2020.

  • Ruchi, A., Dubinkina, S., and de Wiljes, J. (2020). Fast hybrid tempered ensemble transform filter for Bayesianelliptical problems. Nonlin. Processes Geophys., in press, doi:10.5194/npg-2020-24

  • Hamm, M., Pelivan, I., Grott, M., and de Wiljes, J. (2020). Thermophysical modelling and parameter esti-mation of small solar system bodies via data assimilation. Mon. Not. R. Astron. Soc., 496:2776–2785, doi:10.1093/mnras/staa1755

  • Maneugueu, A., Vernade, C., Carpentier A., and Valko, M. (2020). Stochastic bandits with arm-dependent delays. In Thirty-seventh International Conference on Machine Learning, accepted for publication. arXiv:2006.10459

  • A. G. Maneugueu, C. Vernade, A. Carpentier, and M. Valko. Stochastic bandits with arm-dependent delays. In: International Conference on Machine Learning. PMLR. 2020, pp. 3348–3356, 2020. arXiv:2006.10459

  • Schwetlick, L., Rothkegel, L.O.M., Trukenbrod, H.A., Engbert, R. (2020). Modeling the effects of perisaccadic attention on gaze statistics during scene viewing. Communications Biology, 3, 727. doi: 10.1038/s42003-020-01429-8

  • 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

  • Roelly, S. and Zass, A. (2020). Marked Gibbs Point Processes with Unbounded Interaction: An Existence Result. Journal of Statistical Physics 179, 972–996 (2020). Open Access

  • Garbuno-Inigo, A., Nüsken, N., and Reich, S. (2020). Affine invariant interacting Langevin dynamics for Bayesian inference. SIAM J. Dyn. Syst., Vol. 19(3), 1633-1658. doi:10.1137/19M1304891. arXiv:1912.02859

  • Pasemann, G. and Stannat, W. (2020): Drift Estimation for Stochastic Reaction-Diffusion Systems. Electron. J. Statist. 14, no. 1, 547-579. doi:10.1214/19-EJS1665  arXiv 1904.04774

  • Jäger, L. A., Makowski, S., Prasse, P., Liehr, S., Seidler, M., & Scheffer, T. (2019). Deep Eyedentification: Biometric Identification using Micro-Movements of the Eye. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), 299-314. Springer, Cham. doi: 10.1007/978-3-030-46147-8_18

  • 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

  • Seelig, S. A., Rabe, M. M., Malem-Shinitski, N., Risse, S., Reich, S., and Engbert, R. (2020). Bayesian parameter estimation for the SWIFT model of eye-movement control during reading. Journal of Mathematical Psychology, 95, 102313. doi:10.1016/j.jmp.2019.102313; arXiv: 1901.11110.

  • Flemming, S., Font, F., Alonso, S., and Beta, C. (2020). How cortical waves drive fission of motile cells. PNAS, 117(12):6330–6338, doi:10.1073/pnas.1912428117

  • 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

  • de Wiljes, J., Pathiraja, S. and Reich, S. (2020): Ensemble transform algorithms for nonlinear smoothing problems. SIAM J. Scientific Computing, 42, A87-A114. arXiv:1901.06300; doi: 10.1137/19M1239544

  • Maier C., Hartung N., de Wiljes J., Kloft C. and Huisinga W.. Bayesian data assimilation to supportinformed decision-making in individualized chemotherapy. CPT Pharmacometrics Syst. Pharmacol.,9(3):153164, 2020. doi:10.1002/psp4.12492