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20182026
most citedOvercoming the curse of dimensionality in the numerical approximation of high-dimensional semilinear elliptic partial differential equations

30 citations · 58 across the 19 of their papers we have counts for

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8 papers · 1 filter

cs.LG2026

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…

cs.LG2026

Universality and Approximation Rates of Graph Neural Networks with Random Features

Lukas Gonon, Thilo Meyer-Brandis, Niklas Weber

We investigate message-passing graph neural networks with random node features. Random node features are known to enhance the expressiveness of graph neural networks (GNNs) both th…

cs.LG2026

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…

cs.LG2026

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…

cs.LG2024

Universal randomised signatures for generative time series modelling

Francesca Biagini, Lukas Gonon, Niklas Walter

Randomised signature has been proposed as a flexible and easily implementable alternative to the well-established path signature. In this article, we employ randomised signature to…

cs.LG2023

Infinite-dimensional reservoir computing

Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega

Reservoir computing approximation and generalization bounds are proved for a new concept class of input/output systems that extends the so-called generalized Barron functionals to…