Absolutely Continuous Laws of Jump-Diffusions in Finite and Infinite Dimensions with Applications to Mathematical Finance. - Barbara Forster; Eva Lütkebohmert; Josef Teichmann (SIAM J. Math. Anal., 2008)

Term Structure Models Driven by Wiener Processes and Poisson Measures: Existence and Positivity. - Damir Filipovic; Stefan Tappe; Josef Teichmann (SIAM J. Financial Math., 2010)

Polynomial processes and their applications to mathematical finance. - Christa Cuchiero; Martin Keller-Ressel; Josef Teichmann (Finance Stochastics, 2012)

Efficient Simulation and Calibration of General HJM Models by Splitting Schemes. - Philipp Dörsek; Josef Teichmann (SIAM J. Financial Math., 2013)

A convergence result for the Emery topology and a variant of the proof of the fundamental theorem of asset pricing. - Christa Cuchiero; Josef Teichmann (Finance Stochastics, 2015)

Parabolic Free Boundary Price Formation Models Under Market Size Fluctuations. - Peter A. Markowich; Josef Teichmann; Marie-Therese Wolfram (Multiscale Model. Simul., 2016)

Discrete Time Term Structure Theory and Consistent Recalibration Models. - Anja Richter; Josef Teichmann (SIAM J. Financial Math., 2017)

How implicit regularization of Neural Networks affects the learned function - Part I. - Jakob Heiss; Josef Teichmann; Hanna Wutte (CoRR, 2019)

Estimating Full Lipschitz Constants of Deep Neural Networks. - Calypso Herrera; Florian Krach; Josef Teichmann (CoRR, 2020)

Denise: Deep Learning based Robust PCA for Positive Semidefinite Matrices. - Calypso Herrera; Florian Krach; Josef Teichmann (CoRR, 2020)

Theoretical Guarantees for Learning Conditional Expectation using Controlled ODE-RNN. - Calypso Herrera; Florian Krach; Josef Teichmann (CoRR, 2020)

Deep Neural Networks, Generic Universal Interpolation, and Controlled ODEs. - Christa Cuchiero; Martin Larsson; Josef Teichmann (SIAM J. Math. Data Sci., 2020)

Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering. - Calypso Herrera; Florian Krach; Josef Teichmann (ICLR, 2021)

Deep Hedging under Rough Volatility. - Blanka Horvath; Josef Teichmann; Zan Zuric (CoRR, 2021)

Optimal Stopping via Randomized Neural Networks. - Calypso Herrera; Florian Krach; Pierre Ruyssen; Josef Teichmann (CoRR, 2021)

Infinite wide (finite depth) Neural Networks benefit from multi-task learning unlike shallow Gaussian Processes - an exact quantitative macroscopic characterization. - Jakob Heiss; Josef Teichmann; Hanna Wutte (CoRR, 2021)

NOMU: Neural Optimization-based Model Uncertainty. - Jakob Heiss; Jakob Weissteiner; Hanna S. Wutte; Sven Seuken; Josef Teichmann (ICML, 2022)

Randomized Signature Layers for Signal Extraction in Time Series Data. - Enea Monzio Compagnoni; Luca Biggio; Antonio Orvieto; Thomas Hofmann 001; Josef Teichmann (CoRR, 2022)

Applications of Signature Methods to Market Anomaly Detection. - Erdinc Akyildirim; Matteo Gambara; Josef Teichmann; Syang Zhou (CoRR, 2022)

Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs. - Florian Krach; Marc Nübel; Josef Teichmann (CoRR, 2022)

Discrete-Time Signatures and Randomness in Reservoir Computing. - Christa Cuchiero; Lukas Gonon; Lyudmila Grigoryeva; Juan-Pablo Ortega; Josef Teichmann (IEEE Trans. Neural Networks Learn. Syst., 2022)

On the effectiveness of Randomized Signatures as Reservoir for Learning Rough Dynamics. - Enea Monzio Compagnoni; Anna Scampicchio; Luca Biggio; Antonio Orvieto; Thomas Hofmann 001; Josef Teichmann (IJCNN, 2023)

How (Implicit) Regularization of ReLU Neural Networks Characterizes the Learned Function - Part II: the Multi-D Case of Two Layers with Random First Layer. - Jakob Heiss; Josef Teichmann; Hanna Wutte (CoRR, 2023)

Global universal approximation of functional input maps on weighted spaces. - Christa Cuchiero; Philipp Schmocker; Josef Teichmann (CoRR, 2023)

Randomized Signature Methods in Optimal Portfolio Selection. - Erdinc Akyildirim; Matteo Gambara; Josef Teichmann; Syang Zhou (CoRR, 2023)

Denise: Deep Robust Principal Component Analysis for Positive Semidefinite Matrices. - Calypso Herrera; Florian Krach; Anastasis Kratsios; Pierre Ruyssen; Josef Teichmann (Trans. Mach. Learn. Res., 2023)

A case study for unlocking the potential of deep learning in asset-liability-management. - Thomas Krabichler; Josef Teichmann (Frontiers Artif. Intell., 2023)

Adversarial Inverse Reinforcement Learning for Market Making. - Juraj Zelman; Martin Stefanik; Moritz Weiss; Josef Teichmann (ICAIF, 2024)

Robust Utility Optimization via a GAN Approach. - Florian Krach; Josef Teichmann; Hanna Wutte (CoRR, 2024)

Learning Chaotic Systems and Long-Term Predictions with Neural Jump ODEs. - Florian Krach; Josef Teichmann (CoRR, 2024)

Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework. - William Andersson; Jakob Heiss; Florian Krach; Josef Teichmann (Trans. Mach. Learn. Res., 2024)

Signature Reconstruction from Randomized Signatures. - Mie Glückstad; Nicola Muca Cirone; Josef Teichmann (CoRR, 2025)

Universal approximation property of neural stochastic differential equations. - Anna P. Kwossek; David J. Prömel; Josef Teichmann (CoRR, 2025)