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2020 – today
- 2024
[j4]Lorenz Richter, Leon Sallandt, Nikolas Nüsken:
From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs. J. Mach. Learn. Res. 25: 248:1-248:40 (2024)- 2023
- [j3]Christian Bayer
, Martin Eigel, Leon Sallandt, Philipp Trunschke:
Pricing High-Dimensional Bermudan Options with Hierarchical Tensor Formats. SIAM J. Financial Math. 14(2): 383-406 (2023) - [i4]Lorenz Richter, Leon Sallandt, Nikolas Nüsken:
From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs. CoRR abs/2307.15496 (2023) - 2022
- [j2]Konstantin Fackeldey, Mathias Oster, Leon Sallandt, Reinhold Schneider:
Approximative Policy Iteration for Exit Time Feedback Control Problems Driven by Stochastic Differential Equations using Tensor Train Format. Multiscale Model. Simul. 20(1): 379-403 (2022) - [j1]Mathias Oster, Leon Sallandt, Reinhold Schneider:
Approximating Optimal feedback Controllers of Finite Horizon Control Problems Using Hierarchical Tensor Formats. SIAM J. Sci. Comput. 44(3): 746- (2022) - 2021
- [c1]Lorenz Richter, Leon Sallandt, Nikolas Nüsken:
Solving high-dimensional parabolic PDEs using the tensor train format. ICML 2021: 8998-9009 - [i3]Lorenz Richter, Leon Sallandt, Nikolas Nüsken:
Solving high-dimensional parabolic PDEs using the tensor train format. CoRR abs/2102.11830 (2021) - [i2]Christian Bayer, Martin Eigel, Leon Sallandt, Philipp Trunschke:
Pricing high-dimensional Bermudan options with hierarchical tensor formats. CoRR abs/2103.01934 (2021) - [i1]Mathias Oster, Leon Sallandt, Reinhold Schneider:
Approximating optimal feedback controllers of finite horizon control problems using hierarchical tensor formats. CoRR abs/2104.06108 (2021)
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