Publications
Textbook (in German)
- Statistik und maschinelles Lernen. Eine mathematische Einführung in klassische und moderne Methoden with Moritz Jirak, Konstantin Krenz and Markus Reiß, 2021, Springer Sprektrum. Corrections
Preprints
- Calibrating Bayesian Generative Machine Learning for Bayesiamplification with Sebastian Bieringer, Sascha Diefenbacher and Gregor Kasieczka, arXiv: 2408.00838
- A Wasserstein perspective of Vanilla GANs with Lea Kunkel, arXiv: 2403.15312
- Characterization of Besov spaces with dominating mixed smoothness by differences with Paul Nikolaev and David J. Prömel, arXiv: 2403.04469
- Classifier Surrogates: Sharing AI-based Searches with the World with Sebastian Bieringer, Gregor Kasieczka and Jan Kieseler, arXiv: 2402.15558
- AdamMCMC: Combining Metropolis Adjusted Langevin with Momentum-based Optimization with Sebastian Bieringer, Gregor Kasieczka and Maximilian F. Steffen, arXiv: 2312.14027
- Statistical guarantees for stochastic Metropolis-Hastings with Sebastian Bieringer, Gregor Kasieczka and Maximilian F. Steffen, arXiv: 2310.09335
- A PAC-Bayes oracle inequality for sparse neural networks with Maximilian F. Steffen, arXiv: 2204.12392
Publications
You may download preprint versions (arXiv), for the final versions please consult the journals.
- Dimensionality Reduction and Wasserstein Stability for Kernel Regression with Stephan Eckstein and Armin Iske, Journal of Machine Learning Research, 24 (334), 1−35, 2023.
- Dispersal density estimation across scales with Marc Hoffmann, Annals of Statistics, 51 (3), 1258-1281, 2023.
- Nonparametric calibration for stochastic reaction-diffusion equations based on discrete observations with Florian Hildebrandt, Stochastic Processes and their Applications, 162, 171-217, 2023.
- Calomplification - The Power of Generative Calorimeter Models with Sebastian Bieringer, Anja Butter, Sascha Diefenbacher, Engin Eren, Frank Gaede, Daniel Hundhausen, Gregor Kasieczka, Benjamin Nachman and Tilman Plehn, Journal of Instrumentation, 17, P09028, 2022.
- Paracontrolled distribution approach to stochastic Volterra equations with David Prömel, Journal of Differential Equations, 302, 222-272, 2021.
- Parameter estimation for SPDEs based on discrete observations in time and space with Florian Hildebrandt, Electronic Journal of Statistics, 15 (1), 2716-2776, 2021.
- Influence of stiripentol on perampanel serum levels with Nicole Trabs, Stefan Stodieck and Patrick M. House, Epilepsy Research, 164, 106367, 2020.
- Volatility estimation for stochastic PDEs using high-frequency observations with Markus Bibinger, Stochastic Processes and their Applications, 130(5), 3005-3052, 2020.
- On central limit theorems for power variations of the solution to the stochastic heat equation with Markus Bibinger, Stochastic Models, Statistics and Their Applications. Springer Proceedings in Mathematics & Statistics, 294, 69–84, 2019.
- Profiting from correlations: Adjusted estimators for categorial data with Tobias Niebuhr, Applied Stochastic Models in Business and Industry, 35 (4), 1090–1102, 2019.
- Sparse covariance matrix estimation in high-dimensional deconvolution with Denis Belomestny and Alexandre B. Tsybakov, Bernoulli, 25 (3), 1901–1938, 2019.
- Low-rank diffusion matrix estimation for high-dimensional time-changed Lévy processes with Denis Belomestny, Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, 54 (3), 1583–1621, 2018.
- Bayesian inverse problems with unknown operators, Inverse problems, 34 (8), 085001, 2018.
- Adaptive confidence bands for Markov chains and diffusions: Estimating the invariant measure and the drift with Jakob Söhl, ESAIM: Probability and Statistics, 20, 432–462, 2016.
- Spectral estimation for diffusions with random sampling times with Jakub Chorowski, Stochastic Processes and their Applications, 126 (10), 2976–3008, 2016.
- Rough differential equations driven by signals in Besov spaces with David Prömel, Journal of Differential Equations, 260 (6), 5202–5249, 2016.
- High-frequency Donsker theorems for Lévy measures with Richard Nickl, Jakob Söhl and Markus Reiß, Probability Theory and Related Fields, 164(1), 61-108, 2016.
- Adaptive quantile estimation in deconvolution with unknown error distribution with Itai Dattner and Markus Reiß, Bernoulli, 22 (1), 143-192, 2016.
- Information bounds for inverse problems with application to deconvolution and Lévy models, Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, 51(4), 1620-1650, 2015.
- Quantile estimation for Lévy measures, Stochastic Processes and their Applications, 125(9), 3484–3521, 2015.
- Option calibration of exponential Lévy models: Confidence intervals and empirical results with Jakob Söhl, Journal of Computational Finance, 18(2), 91-119, 2014.
- On infinitely divisible distributions with polynomially decaying characteristic functions, Statistics & Probability Letters, 94, 56-62, 2014.
- Calibration of self-decomposable Lévy models, Bernoulli, 20(1), 109–140, 2014.
- A uniform central limit theorem and efficiency for deconvolution estimators with Jakob Söhl, Electronic Journal of Statistics, 6, 2486-2518, 2012.
Theses
- Adaptive and efficient quantile estimation: From deconvolution to Lévy processes, PhD thesis, Humbodlt-Universität zu Berlin, 2014.
- Estimation in self-decomposable Lévy models, Diploma thesis, Humbodlt-Universität zu Berlin, 2011.