Josef Teichmann

According to our database1, Josef Teichmann authored at least 28 papers between 2008 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Robust Utility Optimization via a GAN Approach.
CoRR, 2024

2023
A case study for unlocking the potential of deep learning in asset-liability-management.
Frontiers Artif. Intell., February, 2023

Randomized Signature Methods in Optimal Portfolio Selection.
CoRR, 2023

Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework.
CoRR, 2023

Global universal approximation of functional input maps on weighted spaces.
CoRR, 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.
CoRR, 2023

On the effectiveness of Randomized Signatures as Reservoir for Learning Rough Dynamics.
Proceedings of the International Joint Conference on Neural Networks, 2023

2022
Discrete-Time Signatures and Randomness in Reservoir Computing.
IEEE Trans. Neural Networks Learn. Syst., 2022

Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs.
CoRR, 2022

Applications of Signature Methods to Market Anomaly Detection.
CoRR, 2022

Randomized Signature Layers for Signal Extraction in Time Series Data.
CoRR, 2022

NOMU: Neural Optimization-based Model Uncertainty.
Proceedings of the International Conference on Machine Learning, 2022

2021
Infinite wide (finite depth) Neural Networks benefit from multi-task learning unlike shallow Gaussian Processes - an exact quantitative macroscopic characterization.
CoRR, 2021

Optimal Stopping via Randomized Neural Networks.
CoRR, 2021

Deep Hedging under Rough Volatility.
CoRR, 2021

Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
Deep Neural Networks, Generic Universal Interpolation, and Controlled ODEs.
SIAM J. Math. Data Sci., 2020

Theoretical Guarantees for Learning Conditional Expectation using Controlled ODE-RNN.
CoRR, 2020

Denise: Deep Learning based Robust PCA for Positive Semidefinite Matrices.
CoRR, 2020

Estimating Full Lipschitz Constants of Deep Neural Networks.
CoRR, 2020

2019
How implicit regularization of Neural Networks affects the learned function - Part I.
CoRR, 2019

2017
Discrete Time Term Structure Theory and Consistent Recalibration Models.
SIAM J. Financial Math., 2017

2016
Parabolic Free Boundary Price Formation Models Under Market Size Fluctuations.
Multiscale Model. Simul., 2016

2015
A convergence result for the Emery topology and a variant of the proof of the fundamental theorem of asset pricing.
Finance Stochastics, 2015

2013
Efficient Simulation and Calibration of General HJM Models by Splitting Schemes.
SIAM J. Financial Math., 2013

2012
Polynomial processes and their applications to mathematical finance.
Finance Stochastics, 2012

2010
Term Structure Models Driven by Wiener Processes and Poisson Measures: Existence and Positivity.
SIAM J. Financial Math., 2010

2008
Absolutely Continuous Laws of Jump-Diffusions in Finite and Infinite Dimensions with Applications to Mathematical Finance.
SIAM J. Math. Anal., 2008


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