Adaptive random Fourier features with Metropolis sampling. - Aku Kammonen; Jonas Kiessling; Petr Plechác; Mattias Sandberg; Anders Szepessy (CoRR, 2020)

Smaller generalization error derived for deep compared to shallow residual neural networks. - Aku Kammonen; Jonas Kiessling; Petr Plechác; Mattias Sandberg; Anders Szepessy; Raúl Tempone (CoRR, 2020)

Numerical algorithms for high dimensional integration with application to machine learning and molecular dynamics. - Aku Kammonen (2021)

Comparing Spectral Bias and Robustness For Two-Layer Neural Networks: SGD vs Adaptive Random Fourier Features. - Aku Kammonen; Lisi Liang; Anamika Pandey; Raúl Tempone (CoRR, 2024)

Adaptive Random Fourier Features Training Stabilized By Resampling With Applications in Image Regression. - Aku Kammonen; Anamika Pandey; Erik von Schwerin; Raúl Tempone (CoRR, 2024)

An Adaptive Random Fourier Features approach Applied to Learning Stochastic Differential Equations. - Owen Douglas; Aku Kammonen; Anamika Pandey; Raúl Tempone (CoRR, 2025)