9 citations · 13 across the 15 of their papers we have counts for
6 papers · 1 filter
Adaptive Random Fourier Features Training Stabilized By Resampling With Applications in Image Regression
Aku Kammonen, Anamika Pandey, Erik von Schwerin +1
This paper presents an enhanced adaptive random Fourier features (ARFF) training algorithm for shallow neural networks, building upon the work introduced in "Adaptive Random Fourie…
Forward Propagation of Low Discrepancy Through McKean-Vlasov Dynamics: From QMC to MLQMC
Nadhir Ben Rached, Abdul-Lateef Haji-Ali, Raúl Tempone +1
This work develops a particle system addressing the approximation of McKean-Vlasov stochastic differential equations (SDEs). The novelty of the approach lies in involving low discr…
Continuous time Stochastic optimal control under discrete time partial observations
Christian Bayer, Boualem Djehiche, Eliza Rezvanova +1
This work addresses stochastic optimal control problems where the unknown state evolves in continuous time while partial, noisy, and possibly controllable measurements are only ava…
Importance sampling for rare event tracking within the ensemble Kalman filtering framework
Nadhir Ben Rached, Erik von Schwerin, Gaukhar Shaimerdenova +1
In this work we employ importance sampling (IS) techniques to track a small over-threshold probability of a running maximum associated with the solution of a stochastic differentia…
Quasi-Monte Carlo with Domain Transformation for Efficient Fourier Pricing of Multi-Asset Options
Christian Bayer, Chiheb Ben Hammouda, Antonis Papapantoleon +2
Efficiently pricing multi-asset options poses a significant challenge in quantitative finance. Fourier methods leverage the regularity properties of the integrand in the Fourier do…
Comparing Spectral Bias and Robustness For Two-Layer Neural Networks: SGD vs Adaptive Random Fourier Features
Aku Kammonen, Lisi Liang, Anamika Pandey +1
We present experimental results highlighting two key differences resulting from the choice of training algorithm for two-layer neural networks. The spectral bias of neural networks…