32 citations · 32 across the 1 of their papers we have counts for
6 papers
Distributionally robust approximation property of neural networks
Mihriban Ceylan, David J. Prömel
The universal approximation property uniformly with respect to weakly compact families of measures is established for several classes of neural networks. To that end, we prove that…
Pathwise analysis of log-optimal portfolios
Andrew L. Allan, Anna P. Kwossek, Chong Liu +1
Based on the theory of càdlàg rough paths, we develop a pathwise approach to analyze stability and approximation properties of portfolios along individual price trajectories genera…
A rough path approach to pathwise stochastic integration à la Föllmer
Purba Das, Anna P. Kwossek, David J. Prömel
We develop a general framework for pathwise stochastic integration that extends Föllmer's classical approach beyond gradient-type integrands and standard left-point Riemann sums an…
Universal approximation property of neural stochastic differential equations
Anna P. Kwossek, David J. Prömel, Josef Teichmann
We identify various classes of neural networks that are able to approximate continuous functions locally uniformly subject to fixed global linear growth constraints. For such neura…
Quantitative relative entropy estimates on the whole space for convolution interaction forces
Paul Nikolaev, David J. Prömel
Quantitative estimates are derived, on the whole space, for the relative entropy between the joint law of random interacting particles and the tensorized law at the limiting system…
Local times for typical price paths and pathwise Tanaka formulas
Nicolas Perkowski, David J. Prömel
Following a hedging based approach to model free financial mathematics, we prove that it should be possible to make an arbitrarily large profit by investing in those one-dimensiona…