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

Publications

  • Birzhan Ayanbayev, Ilja Klebanov, Han Cheng Lie and T J Sullivan (2021). Gamma-convergence of Onsager–Machlup functionals: II. convergence of Onsager–Machlup functionals: II. Infinite product measures on Banach spaces. Inverse Problems, Volume 38, Number 2, doi:10.1088/1361-6420/ac3f82.

  • M. Redmann, M.A. Freitag (2021). Optimization based model order reduction for stochastic systems. Appl. Math. Comput., 398.

  • H. C. Lie, M. Stahn, T.J. Sullivan (2022). Randomised one-step time integration methods for deterministic operator differential equations. Calcolo, Volume 59, Number 13 doi:10.1007/s10092-022-00457-6.

  • M.A. Freitag, S. Reich (2022). Datenassimilation: Die nahtlose Verschmelzung von Daten und Modellen. Mitteilungen der Deutschen Mathematiker-VereinigungVerlag, De GruyterSeiten, 108‒112, Band 30.

  • Moldenhawer, T., Moreno, E., Schindler, D., Flemming, S., Holschneider, M., Huisinga, W., Alonso, S. and Beta, C. (2022). Spontaneous transitions between amoeboid and keratocyte-like modes of migration. Front. Cell Dev. Biol., 10:898351, doi:10.3389/fcell.2022.898351

  • Yochelis, A., Flemming, S. and Beta, C. (2022) Versatile Patterns in the Actin Cortex of Motile Cells: Self-Organized Pulses Can Coexist with Macropinocytic Ring-Shaped Waves. Phys. Rev. Lett., 129:088101, doi:10.1103/PhysRevLett.129.088101

  • Schwetlick, L.; Backhaus, D. & Engbert, R. (2022). A dynamical scan-path model for task-dependence during scene viewing. Psychological Review, American Psychological Association (APA),doi: 10.1037/rev0000379

  • Vilk, O., Aghion, E., Avgar, T., Beta, C., Nagel, O., Sabri, A., Sarfati, R., Schwartz, D., Weiss, M., Krapf, D., Nathan, R., Metzler, R. and Assaf, M. (2022) Unravelling the origins of anomalous diffusion: From molecules to migrating storks. Phys. Rev. Research, 4:033055, doi:10.1103/PhysRevResearch.4.033055

  • Riedel, C., Geßner, H., Seegebrecht, A., Ayon, S. I., Chowdhury, S. H., Engbert, R. & Lucke, U., (2022). Including Data Management in Research Culture Increases the Reproducibility of Scientific Results. In: Demmler, D., Krupka, D. & Federrath, H. (Hrsg.), INFORMATIK 2022. Gesellschaft für Informatik, Bonn. (S. 1341-1352). DOI: 10.18420/inf2022_114

  • Moreno, E., Grossmann, R., Beta, C. and Alonso S. (2022) From Single to Collective Motion of Social Amoebae: A Computational Study of Interacting Cells. Front. Phys., 9:750187, doi:10.3389/fphy.2021.750187

  • Alqahtani, A., Mach, T., and Reichel, L. (2023). Solution of Ill-posed Problems with Chebfun. Numerical Algorithms (2023). doi:10.1007/s11075-022-01390-z arXiv 2007.16137

  • Pidstrigach, J. and Reich, S. (2022). Affine-invariant ensemble transform methods for logistic regression. Foundation of Computational Mathematics, 22, doi:10.10007/s10208-022-09550-2.

  • Molkenthin, C., Donner, C., Reich, S., Zöller, G., Hainzl, S., Holschneider, M. and Opper, M. (2022): GP-ETAS: Semiparametric Bayesian inference for the spatio-temporal Epidemic Type Aftershock Sequence model. Statistics and Computation, Vol. 32, 29. doi:10.1007/s11222-022-10085-3.

  • Engbert, R., Rabe, M. M., Schwetlick, L., Seelig, S. A., Reich, S., Vasishth, S. (2022). Data assimilation in dynamical cognitive science. Trends in Cognitive Sciences, 26(2), 99-102, doi:10.1016/j.tics.2021.11.006.

  • Malem-Shinitski, N., Ojeda, C., and Opper, M. (2022). Variational Bayesian Inference for Nonlinear Hawkes Process with Gaussian Process Self-Effects. Entropy, 24(3), 356, doi: 10.3390/e24030356.

  • Mach, T., Reichel, L., and Van Barel, M. (2023). Adaptive cross approximation for Tikhonov regularization in general form. Numerical Algorithms, doi:10.1007/s11075-022-01395-8 arXiv 2204.05740

  • Pathiraja, S., & van Leeuwen, P. J. (2022). Multiplicative non-Gaussian model error estimation in data assimilation. Journal of Advances in Modeling Earth Systems, 14, e2021MS002564. https://doi.org/10.1029/2021MS002564

  • Ruchi, S., Dubinkina, S. and de Wiljes, J. (2021). Fast hybrid tempered ensemble transform filter for Bayesian elliptical problems via Sinkhorn approximation. Nonlinear Processes in Geophysics, 28(1): 23-41 [1]

  • Birzhan Ayanbayev, Ilja Klebanov, Han Cheng Lie and T J Sullivan (2021). Gamma-convergence of Onsager–Machlup functionals: I. With applications to maximum a posteriori estimation in Bayesian inverse problems. Inverse Problems, Volume 38, Number 2, doi:10.1088/1361-6420/ac3f81.

  • 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

  • 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

  • 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

  • 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

  • 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: 10.1137/19M1303162;  arXiv:1911.10832

  • 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

  • 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

  • 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)

  • 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

  • Malem-Shinitski, N., Opper, M., Reich, S., Schwetlick, S., Seelig S. A., & 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

  • 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

  • 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

  • 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. (2019). Drift Estimation for Stochastic Reaction-Diffusion Systems. Electron. J. Statist. 14 (2020), 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

  • 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

  • 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

  • Cervantes, S., Shprits, Y. Y., Aseev, N., Drozdov, A., Castillo, A., and Stolle, C. (2020). Identifying radiation beltelectron source and loss processes by assimilating spacecraft data in a three-dimensional diffusionmodel. J. Geophys. Res.-Space, 125(1):1–16, doi:10.1029/2019JA027514

  • Engbert, R., Rabe, M.M., Seelig, S.A., & Reich, S. (2019). Bayesian parameter estimation for dynamical models of eye-movement control using adaptive Markov Chain Monte Carlo simulations. Forschung im HLRN-Verbund 2019.

  • Gugushvili, S., Mariucci, E. and Meulen, van der F. (2019). Decompounding discrete distributions: A non-parametric Bayesian approach. To appear in Scandinavian Journal of Statistics. arXiv: 1903.11142 

  • Mariucci, E., Ray, K. and Szabó, B. (2019). A Bayesian nonparametric approach to log-concave density estimation. To appear in Bernoulli. arXiv: 1703.09531

  • Pathiraja, S. and Reich, S. (2019). Discrete gradients for computational Bayesian inference. Journal of Computational Dynamics, 6, 385-400. arXiv:1901.06300; doi: 10.3934/jcd.2019019

  • Geßner, H. & Kiy, A., (2019). A mobile campus application as a sensor node for Personal Learning Environments. In: Pinkwart, N. & Konert, J. (Hrsg.), DELFI 2019. Bonn: Gesellschaft für Informatik e.V.. (S. 187-192). DOI: 10.18420/delfi2019_356

  • Blanchard, G. and Zadorozhnyi, O. (2019). Concentration of weakly dependent Banach-valued sums and applications to statistical learning methods. Bernoulli, 25(4B), 3421-3458. doi:10.3150/18-BEJ1095 (arXiv: 1712.01934)

  • Castillo, A. M., Shprits, Y. Y., Ganushkina, N., Drozdov, A., Aseev, N., Wang, D. and Dubyagin, S. (2019). Simulations of the inner magnetospheric energetic electrons using the IMPTAM-VERB coupled model. Journal of Atmospheric and Solar-Terrestrial Physics. doi: 10.1016/j.jastp.2019.05.014 

  • Malem-Shinitski, N., Seelig, S. A., Reich, S. and Engbert, R. (2019). Bayesian inference for an exploration-exploitation model of human gaze control. Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany (extended abstract). doi:10.32470/CCN.2019.1246-0

  • Seelig, S. A., Rabe, M. M., Malem-Shinitski, N., Reich, S., Engbert, R. (2019). Parameter estimation for the SWIFT model of eye-movement control during reading. Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany (extended abstract) doi:10.32470/CCN.2019.1369-0

  • Shcherbakov, R., Zhuang, J., Zöller, G. and Ogata, Y. (2019). Forecasting the magnitude of the largest expected earthquake, Nature Communications, 10, nr. 4051. doi: 10.1038/s41467-019-11958-4

  • Cvetković N., Conrad T., and Lie H.C. (2019). Convergent discretisation schemes for transition path theory for diffusion processes (2019). SIAM Multiscale Modelling and Simulation 19(1), 242–266. doi.org/10.1137/20M1329354; arXiv:1907.05799

  • Götze, F., Naumov, A., Spokoiny, V. and Ulyanov, V. (2019). Gaussian comparison and anti-concentration inequalities for norms of Gaussian random elements, Bernoulli, in print. arXiv:1708.08663

  • Ty, A.J.A., Fang, Z., Gonzales, R.A., Rozdeba, P.J. and Abarbanel, H.D.I. (2019), Machine Learning of Time Series Using Time-delay Embedding and Precision Annealing. Neural Computation Vol. 31(10), 2004-2024. doi:10.1162/neco_a_01224. arXiv:1902.05062

  • Trukenbrod, H. A., Barthelmé, S., Wichmann, F. A. and Engbert, R. (2019). Spatial statistics for gaze patterns in scene viewing: Effects of repeated viewing, Journal of Vision, 19(6):5, 1-19. doi: 10.1167/19.6.5

  • Somogyvári, M. and Reich, S. (2019). Convergence tests for transdimensional Markov chains in geoscience imaging, Math Geosci, 2019. doi: 10.1007/s11004-019-09811-x

  • Nüsken, N., Reich, S. and Rozdeba, P. J. (2019). State and parameter estimation from observed signal increments, Entropy, Vol. 21(5), 505. arXiv:1903.10717 ;  doi: 10.3390/e21050505

  • Lontsi, A. M., García-Jerez, A., Molina-Villegas, J. C., Sánchez-Sesma, F. J., Molkenthin, C., Ohrnberger, M., Krüger, F., Wang, R. and Fäh, D. (2019). A generalized theory for full microtremor horizontal-to-vertical [H/V(z, f)] spectral ratio interpretation in offshore and onshore environments, Geophysical Journal International, 218(2), 1276–1297. doi: 10.1093/gji/ggz223 arXiv: 1907.04606 

  • Achddou, J., Lam-Weil, J., Carpentier, A. and Blanchard, G. (2019). A minimax near-optimal algorithm for adaptive rejection sampling. Proceedings of the 30th International Conference on Algorithmic Learning Theory, PMLR 98:94-126, 2019. Open Access

  • Reich, S. (2019). Data assimilation: The Schrödinger perspective. Acta Numerica, 28, 635-711. arXiv:1807.08351; doi:10.1017/S0962492919000011

  • Locatelli, A., Carpentier, A., and Valko, M. (2019). Active multiple matrix completion with adaptive confidence sets. Proceedings of Machine Learning Research, PMLR, 89, 1783-1791. Open Access

  • Seznec, J, Locatelli, A., Carpentier, A., Lazaric, A., and Valko, M. (2019). Rotting bandits are no harder than stochastic ones. Proceedings of Machine Learning Research, in PMLR 89:2564-2572. Open Access

  • Leeuwen, P. J. v., Künsch, H.-R., Nerger, L., Potthast, R. and Reich, S. (2019). Particle filters for high-dimensional geoscience applications: a review. Quarterly J Royal Meteorlog. Soc., 145, 2335-2365. arXiv: 1807.10434v2 doi: 10.1002/qj.3551

  • Katz-Samuels, J., Blanchard, G. and Scott, C. (2019). Decontamination of Mutual Contamination Models. Journal of Machine Learning Research (41):1−57, 2019 Open Access

  • Aseev, N. A. and Shprits, Y. Y. (2019). Reanalysis of ring current electron phase space densities using Van AllenProbe observations, convection model, and log-normal Kalman Filter. Space Weather, 17(4):619–638, doi:10.1029/2018SW002110

  • Salamat, M., Zöller, G. and Amini, M. (2019). Prediction of the Maximum Expected Earthquake Magnitude in Iran: From a Catalog with Varying Magnitude of Completeness and Uncertain Magnitudes, Pure and Applied Geophysics, 176 (8): 3425–3438. doi: 10.1007/s00024-019-02141-3

  • Rothkegel, L. O., Schütt, H. H., Trukenbrod, H. A., Wichmann, F. A. and Engbert, R. (2019). Searchers adjust their eye-movement dynamics to target characteristics in natural scenes. Scientific Reports, 9, article no. 1635. doi: 10.1038/s41598-018-37548-w

  • Opper, M. (2019). Variational inference for stochastic differential equations. Ann. Phys., 531(3):1800233, doi:10.1002/andp.201800233

  • Aseev, N. A., Shprits, Y. Y., Wang, D., Wygant, J., Drozdov, A. Y., Kellerman, A. C., and Reeves, G. D. (2019). Transport and loss of ring current electrons inside geosynchronous orbit during the 17 March 2013 storm. J. Geophys. Res.-Space, 124(2):915–933. doi:10.1029/2018JA026031

  • Blanchard, G., Neuvial, P. and Roquain, E. (2019). Post hoc inference via joint family-wise error rate control. (to appear in Annals of Statistics) arXiv: 1703.02307

  • Donner, C. and Opper, M. (2018). Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes, Journal of Machine Learning Research 19, no 67, 1-34. Open Access

  • Morzfeld, M. and Reich, S. (2018). Data assimilation: mathematics for merging models and data. Snapshots of modern mathematics from Oberwolfach, 11. doi: 10.14760/SNAP-2018-011-EN

  • Makowski, S., Jäger, L., Abdelwahab, A., Landwehr, N. and Scheffer, T. (2018). A discriminative model for identifying readers and assessing text comprehension from eye movements. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD). doi: 10.1007/978-3-030-10925-7_13, arxiv preprint: https://arxiv.org/pdf/1809.08031.pdf

  • Blanchard, G., Hoffmann, M. and Reiß, M. (2018). Early stopping for statistical inverse problems via truncated SVD estimation. Electron. J. Statist. 12 (2): 3204-3231. arXiv 1710.07278; doi: 10.1214/18-EJS1482

  • Blanchard, G. and Mücke, N. (2018). Parallelizing Spectral Algorithms for Kernel Learning. Journal of Machine Learning Research (30):1-29, 2018. Open Access

  • Alonso, S., Stange, M. and Beta, C. (2018). Modeling random crawling, membrane deformation and intracellular polarity of motile amoeboid cells. PLoS ONE, 13(8): e0201977. doi: 10.1371/journal.pone.0201977

  • Fiedler, B., Hainzl, S., Zöller, G. and Holschneider, M. (2018). Detection of Gutenberg–Richter b-value changes in earthquake time series, Bulletin of the Seismological Society of America, 108(5A), 2778–2787. doi: 10.1785/0120180091

  • Salamat, M., Zöller, G. Zare, M. and Amini, M. (2018). The maximum expected earthquake magnitudes in different future time intervals of six seismotectonic zones of Iran and its surroundings, Journal of Seismology, 22, 1485–1498. doi: 10.1007/s10950-018-9780-7

  • Cherstvy, A. G., Nagel, O., Beta, C. and Metzler, R. (2018). Non-Gaussianity, population heterogeneity, and transient superdiffusion in the spreading dynamics of amoeboid cells. doi: 10.1039/c8cp04254c

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