Lukas Gonon

According to our database, Lukas Gonon authored at least 23 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Approximation Rates for Deep Calibration of (Rough) Stochastic Volatility Models.

Francesca Biagini, Lukas Gonon, Niklas Walter

SIAM J. Financial Math., 2024

Infinite-dimensional reservoir computing.

Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega

Neural Networks, 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

Variance Norms for Kernelized Anomaly Detection.

Thomas Cass, Lukas Gonon, Nikita Zozoulenko

CoRR, 2024

Universal randomised signatures for generative time series modelling.

Francesca Biagini, Lukas Gonon, Niklas Walter

CoRR, 2024

Fast Deep Hedging with Second-Order Optimization.

Konrad Mueller, Amira Akkari, Lukas Gonon, Ben Wood

Proceedings of the 5th ACM International Conference on AI in Finance, 2024

2023

Random Feature Neural Networks Learn Black-Scholes Type PDEs Without Curse of Dimensionality.

Lukas Gonon

J. Mach. Learn. Res., 2023

Universal Approximation Theorem and error bounds for quantum neural networks and quantum reservoirs.

Lukas Gonon, Antoine Jacquier

CoRR, 2023

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

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

Reservoir kernels and Volterra series.

Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega

CoRR, 2022

Deep neural network expressivity for optimal stopping problems.

Lukas Gonon

CoRR, 2022

2021

Fading memory echo state networks are universal.

Lukas Gonon, Juan-Pablo Ortega

Neural Networks, 2021

Deep ReLU Neural Network Approximation for Stochastic Differential Equations with Jumps.

Lukas Gonon, Christoph Schwab

CoRR, 2021

Deep ReLU Network Expression Rates for Option Prices in high-dimensional, exponential Lévy models.

Lukas Gonon, Christoph Schwab

CoRR, 2021

2020

Reservoir Computing Universality With Stochastic Inputs.

Lukas Gonon, Juan-Pablo Ortega

IEEE Trans. Neural Networks Learn. Syst., 2020

Weak error analysis for stochastic gradient descent optimization algorithms.

Aritz Bercher, Lukas Gonon, Arnulf Jentzen, Diyora Salimova

CoRR, 2020

Memory and forecasting capacities of nonlinear recurrent networks.

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

Approximation Bounds for Random Neural Networks and Reservoir Systems.

Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega

CoRR, 2020

2019

Uniform error estimates for artificial neural network approximations for heat equations.

Lukas Gonon, Philipp Grohs, Arnulf Jentzen, David Kofler, David Siska

CoRR, 2019

Risk bounds for reservoir computing.

Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega

CoRR, 2019

2012

Online model estimation of ultra-wideband TDOA measurements for mobile robot localization.

Amanda Prorok, Lukas Gonon, Alcherio Martinoli

Proceedings of the IEEE International Conference on Robotics and Automation, 2012