2 papers
stat.ML2026
Hybrid least squares for learning functions from highly noisy data
Ben Adcock, Bernhard Hientzsch, Akil Narayan +1
Motivated by the need for efficient estimation of conditional expectations, we consider a least-squares function approximation problem with heavily polluted data. Existing methods…
q-fin.CP2025
Enforcing asymptotic behavior with DNNs for approximation and regression in finance
Hardik Routray, Bernhard Hientzsch
We propose a simple methodology to approximate functions with given asymptotic behavior by specifically constructed terms and an unconstrained deep neural network (DNN). The method…