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Mathias Trabs

Professor Department of Mathematical Statistics

Heidelberg University
Institute for Mathematics
MΛTHEMΛTIKON
Im Neuenheimer Feld 205
69120 Heidelberg, Germany
Phone: +49 (0) 6221 / 54 - 14180
E-mail: mathias.trabs@uni-heidelberg.de

Articles

1. Bieringer, S.; Kasieczka, G.; Steffen, M. F.; Trabs, M.
   The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks
   2026. Journal of Machine Learning Research, 27 (1), 1–50. jmlr.org.
2. Steffen, M. F.; Trabs, M.
   A PAC-Bayes Oracle Inequality for Sparse Neural Networks
   2026. Applications of Mathematics in Sciences, Engineering, and Economics – MathSEE Symposium, Karlsruhe, September 27–29, 2023. Ed.: A. Ott, 131–151, Springer Nature Switzerland. doi:10.1007/978-3-032-01279-1_7.
3. Nikolaev, P.; Prömel, D. J.; Trabs, M.
   Characterization of Besov spaces with dominating mixed smoothness by differences
   2025. Mathematische Nachrichten, 298 (7), 2116–2151. doi:10.1002/mana.202400122.
4. Ehrenreich-Petersen, E.; Massani, B.; Engler, T.; Pardo, O. S.; Glazyrin, K.; Giordano, N.; Hagemann, J.; Sneed, D.; Fedotenko, T.; Campbell, D. J.; Wendt, M.; Wenz, S.; Schroer, C. G.; Trabs, M.; McWilliams, R. S.; Liermann, H.-P.; Jenei, Z.; O’Bannon, E. F.
   X-ray phase contrast imaging and diffraction in the laser-heated diamond anvil cell: A case study on the high-pressure melting of Pt
   2025. Results in Physics, 69, Art.-Nr.: 108132. doi:10.1016/j.rinp.2025.108132.
5. Kunkel, L.; Trabs, M.
   A Wasserstein perspective of Vanilla GANs
   2025. Neural Networks, 181, 106770. doi:10.1016/j.neunet.2024.106770.
6. Bieringer, S.; Diefenbacher, S.; Kasieczka, G.; Trabs, M.
   Calibrating Bayesian generative machine learning for Bayesiamplification
   2024. Machine Learning: Science and Technology, 5 (4), Art.-Nr.: 045044. doi:10.1088/2632-2153/ad9136.
7. Bieringer, S.; Kasieczka, G.; Kieseler, J.; Trabs, M.
   Classifier surrogates: sharing AI-based searches with the world
   2024. The European Physical Journal C, 84 (9), Art.-Nr.: 972. doi:10.1140/epjc/s10052-024-13353-w.
8. Hildebrandt, F.; Trabs, M.
   Nonparametric calibration for stochastic reaction–diffusion equations based on discrete observations
   2023. Stochastic Processes and their Applications, 162, 171–217. doi:10.1016/j.spa.2023.04.019.
9. Hoffmann, M.; Trabs, M.
   Dispersal density estimation across scales
   2023. The Annals of Statistics, 51 (3), 1258–1281. doi:10.1214/23-AOS2290.
10. Eckstein, S.; Iske, A.; Trabs, M.
    Dimensionality Reduction and Wasserstein Stability for Kernel Regression
    2023. Journal of Machine Learning Research, 24. jmlr.org.
11. Bieringer, S.; Butter, A.; Diefenbacher, S.; Eren, E.; Gaede, F.; Hundhausen, D.; Kasieczka, G.; Nachman, B.; Plehn, T.; Trabs, M.
    Calomplification — the power of generative calorimeter models
    2022. Journal of Instrumentation, 17 (09), Art.Nr. P09028. doi:10.1088/1748-0221/17/09/P09028.
12. Prömel, D. J.; Trabs, M.
    Paracontrolled distribution approach to stochastic Volterra equations
    2021. Journal of Differential Equations, 302, 222–272. doi:10.1016/j.jde.2021.08.031.
13. Hildebrandt, F.; Trabs, M.
    Parameter estimation for SPDEs based on discrete observations in time and space
    2021. Electronic Journal of Statistics, 15 (1), 2716–2776. doi:10.1214/21-EJS1848.
14. Trabs, N.; Trabs, M.; Stodieck, S.; House, P. M.
    Influence of stiripentol on perampanel serum levels
    2020. Epilepsy Research, 164, Article no: 106367. doi:10.1016/j.eplepsyres.2020.106367.
15. Bibinger, M.; Trabs, M.
    Volatility estimation for stochastic PDEs using high-frequency observations
    2020. Stochastic Processes and their Applications, 130 (5), 3005–3052. doi:10.1016/j.spa.2019.09.002.
16. Belomestny, D.; Trabs, M.; Tsybakov, A. B.
    Sparse covariance matrix estimation in high-dimensional deconvolution
    2019. Bernoulli, 25 (3). doi:10.3150/18-BEJ1040A.
17. Niebuhr, T.; Trabs, M.
    Profiting from correlations: Adjusted estimators for categorical data
    2019. Applied Stochastic Models in Business and Industry, 35 (4), 1090–1102. doi:10.1002/asmb.2452.
18. Bibinger, M.; Trabs, M.
    On Central Limit Theorems for Power Variations of the Solution to the Stochastic Heat Equation
    2019. Stochastic Models, Statistics and Their Applications – Dresden, Germany, March 2019. Ed.: A. Steland, 69–84, Springer International Publishing. doi:10.1007/978-3-030-28665-1_5.
19. Belomestny, D.; Trabs, M.
    Low-rank diffusion matrix estimation for high-dimensional time-changed Lévy processes
    2018. Annales de l’Institut Henri Poincaré, Probabilités et Statistiques, 54 (3), 1584–1621. doi:10.1214/17-AIHP849.
20. Trabs, M.
    Bayesian inverse problems with unknown operators
    2018. Inverse Problems, 34 (8), Article no: 085001. doi:10.1088/1361-6420/aac3aa.
21. Chorowski, J.; Trabs, M.
    Spectral estimation for diffusions with random sampling times
    2016. Stochastic Processes and their Applications, 126 (10), 2976–3008. doi:10.1016/j.spa.2016.03.009.
22. Prömel, D. J.; Trabs, M.
    Rough differential equations driven by signals in Besov spaces
    2016. Journal of Differential Equations, 260 (6), 5202–5249. doi:10.1016/j.jde.2015.12.012.
23. Nickl, R.; Reiß, M.; Söhl, J.; Trabs, M.
    High-frequency Donsker theorems for Lévy measures
    2016. Probability Theory and Related Fields, 164 (1-2), 61–108. doi:10.1007/s00440-014-0607-3.
24. Dattner, I.; Reiß, M.; Trabs, M.
    Adaptive quantile estimation in deconvolution with unknown error distribution
    2016. Bernoulli, 22 (1). doi:10.3150/14-BEJ626.
25. Söhl, J.; Trabs, M.
    Adaptive confidence bands for Markov chains and diffusions: Estimating the invariant measure and the drift
    2016. ESAIM: Probability and Statistics, 20, 432–462. doi:10.1051/ps/2016017.
26. Trabs, M.
    Information bounds for inverse problems with application to deconvolution and Lévy models
    2015. Annales de l’Institut Henri Poincaré, Probabilités et Statistiques, 51 (4), 1620–1650. doi:10.1214/14-AIHP627.
27. Trabs, M.
    Quantile estimation for Lévy measures
    2015. Stochastic Processes and their Applications, 125 (9), 3484–3521. doi:10.1016/j.spa.2015.04.004.
28. Söhl, J.; Trabs, M.
    Option calibration of exponential Lévy models: confidence intervals and empirical results
    2014. The Journal of Computational Finance, 18 (2), 91–119. doi:10.21314/JCF.2014.275.
29. Trabs, M.
    On infinitely divisible distributions with polynomially decaying characteristic functions
    2014. Statistics & Probability Letters, 94, 56–62. doi:10.1016/j.spl.2014.07.002.
30. Trabs, M.
    Calibration of self-decomposable Lévy models
    2014. Bernoulli, 20 (1). doi:10.3150/12-BEJ478.
31. Söhl, J.; Trabs, M.
    A uniform central limit theorem and efficiency for deconvolution estimators
    2012. Electronic Journal of Statistics, 6, 2486–2518. doi:10.1214/12-EJS757.