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2020 – today
- 2024
[j12]William Andersson, Jakob Heiss, Florian Krach, Josef Teichmann:
Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework. Trans. Mach. Learn. Res. 2024 (2024)- [i18]Florian Krach, Josef Teichmann, Hanna Wutte:
Robust Utility Optimization via a GAN Approach. CoRR abs/2403.15243 (2024) - [i17]Florian Krach, Josef Teichmann:
Learning Chaotic Systems and Long-Term Predictions with Neural Jump ODEs. CoRR abs/2407.18808 (2024) - 2023
- [j11]Thomas Krabichler, Josef Teichmann:
A case study for unlocking the potential of deep learning in asset-liability-management. Frontiers Artif. Intell. 6 (2023) - [j10]Calypso Herrera, Florian Krach, Anastasis Kratsios, Pierre Ruyssen, Josef Teichmann:
Denise: Deep Robust Principal Component Analysis for Positive Semidefinite Matrices. Trans. Mach. Learn. Res. 2023 (2023) - [c3]Enea Monzio Compagnoni, Anna Scampicchio, Luca Biggio, Antonio Orvieto, Thomas Hofmann, Josef Teichmann:
On the effectiveness of Randomized Signatures as Reservoir for Learning Rough Dynamics. IJCNN 2023: 1-8 - [i16]Jakob Heiss
, Josef Teichmann, Hanna Wutte:
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 abs/2303.11454 (2023) - [i15]Christa Cuchiero, Philipp Schmocker, Josef Teichmann:
Global universal approximation of functional input maps on weighted spaces. CoRR abs/2306.03303 (2023) - [i14]William Andersson, Jakob Heiss, Florian Krach, Josef Teichmann:
Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework. CoRR abs/2307.13147 (2023) - [i13]Erdinc Akyildirim, Matteo Gambara, Josef Teichmann, Syang Zhou:
Randomized Signature Methods in Optimal Portfolio Selection. CoRR abs/2312.16448 (2023) - 2022
- [j9]Christa Cuchiero, Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega, Josef Teichmann:
Discrete-Time Signatures and Randomness in Reservoir Computing. IEEE Trans. Neural Networks Learn. Syst. 33(11): 6321-6330 (2022) - [c2]Jakob Heiss, Jakob Weissteiner, Hanna S. Wutte, Sven Seuken, Josef Teichmann:
NOMU: Neural Optimization-based Model Uncertainty. ICML 2022: 8708-8758 - [i12]Enea Monzio Compagnoni, Luca Biggio, Antonio Orvieto, Thomas Hofmann, Josef Teichmann:
Randomized Signature Layers for Signal Extraction in Time Series Data. CoRR abs/2201.00384 (2022) - [i11]Erdinc Akyildirim, Matteo Gambara, Josef Teichmann, Syang Zhou:
Applications of Signature Methods to Market Anomaly Detection. CoRR abs/2201.02441 (2022) - [i10]Florian Krach, Marc Nübel, Josef Teichmann:
Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs. CoRR abs/2206.14284 (2022) - 2021
- [c1]Calypso Herrera, Florian Krach, Josef Teichmann:
Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering. ICLR 2021 - [i9]Blanka Horvath, Josef Teichmann, Zan Zuric:
Deep Hedging under Rough Volatility. CoRR abs/2102.01962 (2021) - [i8]Jakob Heiss, Jakob Weissteiner, Hanna Wutte, Sven Seuken, Josef Teichmann:
NOMU: Neural Optimization-based Model Uncertainty. CoRR abs/2102.13640 (2021) - [i7]Calypso Herrera, Florian Krach, Pierre Ruyssen, Josef Teichmann:
Optimal Stopping via Randomized Neural Networks. CoRR abs/2104.13669 (2021) - [i6]Jakob Heiss, Josef Teichmann, Hanna Wutte:
Infinite wide (finite depth) Neural Networks benefit from multi-task learning unlike shallow Gaussian Processes - an exact quantitative macroscopic characterization. CoRR abs/2112.15577 (2021) - 2020
- [j8]Christa Cuchiero, Martin Larsson, Josef Teichmann:
Deep Neural Networks, Generic Universal Interpolation, and Controlled ODEs. SIAM J. Math. Data Sci. 2(3): 901-919 (2020) - [i5]Calypso Herrera, Florian Krach, Josef Teichmann:
Estimating Full Lipschitz Constants of Deep Neural Networks. CoRR abs/2004.13135 (2020) - [i4]Calypso Herrera, Florian Krach, Josef Teichmann:
Denise: Deep Learning based Robust PCA for Positive Semidefinite Matrices. CoRR abs/2004.13612 (2020) - [i3]Calypso Herrera, Florian Krach, Josef Teichmann:
Theoretical Guarantees for Learning Conditional Expectation using Controlled ODE-RNN. CoRR abs/2006.04727 (2020) - [i2]Christa Cuchiero, Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega, Josef Teichmann:
Discrete-time signatures and randomness in reservoir computing. CoRR abs/2010.14615 (2020)
2010 – 2019
- 2019
- [i1]Jakob Heiss, Josef Teichmann, Hanna Wutte:
How implicit regularization of Neural Networks affects the learned function - Part I. CoRR abs/1911.02903 (2019) - 2017
- [j7]Anja Richter, Josef Teichmann:
Discrete Time Term Structure Theory and Consistent Recalibration Models. SIAM J. Financial Math. 8(1): 504-531 (2017) - 2016
- [j6]Peter A. Markowich, Josef Teichmann, Marie-Therese Wolfram:
Parabolic Free Boundary Price Formation Models Under Market Size Fluctuations. Multiscale Model. Simul. 14(4): 1211-1237 (2016) - 2015
- [j5]Christa Cuchiero, Josef Teichmann:
A convergence result for the Emery topology and a variant of the proof of the fundamental theorem of asset pricing. Finance Stochastics 19(4): 743-761 (2015) - 2013
- [j4]Philipp Dörsek, Josef Teichmann:
Efficient Simulation and Calibration of General HJM Models by Splitting Schemes. SIAM J. Financial Math. 4(1): 575-598 (2013) - 2012
- [j3]Christa Cuchiero, Martin Keller-Ressel, Josef Teichmann:
Polynomial processes and their applications to mathematical finance. Finance Stochastics 16(4): 711-740 (2012) - 2010
- [j2]Damir Filipovic, Stefan Tappe, Josef Teichmann:
Term Structure Models Driven by Wiener Processes and Poisson Measures: Existence and Positivity. SIAM J. Financial Math. 1(1): 523-554 (2010)
2000 – 2009
- 2008
- [j1]Barbara Forster, Eva Lütkebohmert, Josef Teichmann:
Absolutely Continuous Laws of Jump-Diffusions in Finite and Infinite Dimensions with Applications to Mathematical Finance. SIAM J. Math. Anal. 40(5): 2132-2153 (2008)
Coauthor Index
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last updated on 2024-10-07 21:24 CEST by the dblp team
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