Online model estimation of ultra-wideband TDOA measurements for mobile robot localization. - Amanda Prorok; Lukas Gonon; Alcherio Martinoli (ICRA, 2012)

Risk bounds for reservoir computing. - Lukas Gonon; Lyudmila Grigoryeva; Juan-Pablo Ortega (CoRR, 2019)

Uniform error estimates for artificial neural network approximations for heat equations. - Lukas Gonon; Philipp Grohs; Arnulf Jentzen; David Kofler; David Siska (CoRR, 2019)

Approximation Bounds for Random Neural Networks and Reservoir Systems. - Lukas Gonon; Lyudmila Grigoryeva; Juan-Pablo Ortega (CoRR, 2020)

Overcoming the curse of dimensionality in the numerical approximation of high-dimensional semilinear elliptic partial differential equations. - Christian Beck; Lukas Gonon; Arnulf Jentzen (CoRR, 2020)

Memory and forecasting capacities of nonlinear recurrent networks. - Lukas Gonon; Lyudmila Grigoryeva; Juan-Pablo Ortega (CoRR, 2020)

Weak error analysis for stochastic gradient descent optimization algorithms. - Aritz Bercher; Lukas Gonon; Arnulf Jentzen; Diyora Salimova (CoRR, 2020)

Reservoir Computing Universality With Stochastic Inputs. - Lukas Gonon; Juan-Pablo Ortega (IEEE Trans. Neural Networks Learn. Syst., 2020)

Deep ReLU Network Expression Rates for Option Prices in high-dimensional, exponential Lévy models. - Lukas Gonon; Christoph Schwab (CoRR, 2021)

Deep ReLU Neural Network Approximation for Stochastic Differential Equations with Jumps. - Lukas Gonon; Christoph Schwab (CoRR, 2021)

Fading memory echo state networks are universal. - Lukas Gonon; Juan-Pablo Ortega (Neural Networks, 2021)

Deep neural network expressivity for optimal stopping problems. - Lukas Gonon (CoRR, 2022)

Reservoir kernels and Volterra series. - Lukas Gonon; Lyudmila Grigoryeva; Juan-Pablo Ortega (CoRR, 2022)

Discrete-Time Signatures and Randomness in Reservoir Computing. - Christa Cuchiero; Lukas Gonon; Lyudmila Grigoryeva; Juan-Pablo Ortega; Josef Teichmann (IEEE Trans. Neural Networks Learn. Syst., 2022)

The necessity of depth for artificial neural networks to approximate certain classes of smooth and bounded functions without the curse of dimensionality. - Lukas Gonon; Robin Graeber; Arnulf Jentzen (CoRR, 2023)

Random Feature Neural Networks Learn Black-Scholes Type PDEs Without Curse of Dimensionality. - Lukas Gonon (J. Mach. Learn. Res., 2023)

Fast Deep Hedging with Second-Order Optimization. - Konrad Mueller; Amira Akkari; Lukas Gonon; Ben Wood (ICAIF, 2024)

Universal randomised signatures for generative time series modelling. - Francesca Biagini; Lukas Gonon; Niklas Walter (CoRR, 2024)

Variance Norms for Kernelized Anomaly Detection. - Thomas Cass; Lukas Gonon; Nikita Zozoulenko (CoRR, 2024)

An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning. - Lukas Gonon; Arnulf Jentzen; Benno Kuckuck; Siyu Liang; Adrian Riekert; Philippe von Wurstemberger (CoRR, 2024)

Computing Systemic Risk Measures with Graph Neural Networks. - Lukas Gonon; Thilo Meyer-Brandis; Niklas Weber (CoRR, 2024)

Infinite-dimensional reservoir computing. - Lukas Gonon; Lyudmila Grigoryeva; Juan-Pablo Ortega (Neural Networks, 2024)

Approximation Rates for Deep Calibration of (Rough) Stochastic Volatility Models. - Francesca Biagini; Lukas Gonon; Niklas Walter (SIAM J. Financial Math., 2024)

Operator Deep Smoothing for Implied Volatility. - Ruben Wiedemann; Antoine Jacquier; Lukas Gonon (ICLR, 2025)

Random Feature Representation Boosting. - Nikita Zozoulenko; Thomas Cass; Lukas Gonon (CoRR, 2025)

Feedback-driven recurrent quantum neural network universality. - Lukas Gonon; Rodrigo Martínez-Peña; Juan-Pablo Ortega (CoRR, 2025)

Universal Approximation Theorem and Error Bounds for Quantum Neural Networks and Quantum Reservoirs. - Lukas Gonon; Antoine Jacquier (IEEE Trans. Neural Networks Learn. Syst., 2025)