From the 1 of 17 linked papers with an AI index.
17 papers
Robust Control under Stationary Ambiguity
Konrad J. Mueller, Amira Akkari, Ben Wood +1
Control policies optimized in simulation can perform poorly in the real system when the parameters of the simulator are estimated from limited data but the resulting parameter…
Universality and Approximation Rates of Graph Neural Networks with Random Features
Lukas Gonon, Thilo Meyer-Brandis, Niklas Weber
The paper proves that message‑passing graph neural networks with partially random node features can universally approximate any permutation‑invariant or equivariant function on fix…
Neural Slack Variables for Shape Constraints
Ruben Wiedemann, Antoine Jacquier, Lukas Gonon
Enforcing functional inequality constraints such as monotonicity and convexity in neural networks is a fundamental challenge in many industrial and scientific applications. Classic…
Generating Financial Time Series by Matching Random Convolutional Features
Konrad J. Mueller, Nikita Zozoulenko, Ben Wood +2
Generating realistic financial time series is challenging as training data is often limited to a single historical path. With such scarce data, overfitting is hard to avoid, especi…
Random Neural Network Expressivity for Non-Linear Partial Differential Equations
Muhammed Ali Mehmood, Lukas Gonon
Neural networks with randomly generated hidden weights (RaNNs) have been extensively studied, both as a standalone learning method and as an initialization for fully trainable deep…
Quantitative Universal Approximation for Noisy Quantum Neural Networks
Lukas Gonon, Antoine Jacquier, Marcel Mordarski
We provide here a universal approximation theorem with precise quantitative error bounds for noisy quantum neural networks. We focus on applications to Quantitative Finance, where…