Lukas Gonon

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 25

Top Topics

Differential Equations

Error Estimates

Wavelet Synopses

Top Venues

IEEE Trans. Neural Networks Learn. Syst.

Neural Networks

J. Mach. Learn. Res.

Publications

Francesca Biagini, Lukas Gonon, Niklas Walter
Universal randomised signatures for generative time series modelling. CoRR (2024)
Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega
Infinite-dimensional reservoir computing. Neural Networks 179 (2024)
Thomas Cass, Lukas Gonon, Nikita Zozoulenko
Variance Norms for Kernelized Anomaly Detection. CoRR (2024)
Francesca Biagini, Lukas Gonon, Niklas Walter
Approximation Rates for Deep Calibration of (Rough) Stochastic Volatility Models. CoRR (2023)
Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega
Infinite-dimensional reservoir computing. CoRR (2023)
Lukas Gonon
Random Feature Neural Networks Learn Black-Scholes Type PDEs Without Curse of Dimensionality. J. Mach. Learn. Res. 24 (2023)
Lukas Gonon, Robin Graeber, Arnulf Jentzen
The necessity of depth for artificial neural networks to approximate certain classes of smooth and bounded functions without the curse of dimensionality. CoRR (2023)
Lukas Gonon, Antoine Jacquier
Universal Approximation Theorem and error bounds for quantum neural networks and quantum reservoirs. CoRR (2023)
Christa Cuchiero, Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega, Josef Teichmann
Discrete-Time Signatures and Randomness in Reservoir Computing. IEEE Trans. Neural Networks Learn. Syst. 33 (11) (2022)
Lukas Gonon
Deep neural network expressivity for optimal stopping problems. CoRR (2022)
Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega
Reservoir kernels and Volterra series. CoRR (2022)
Lukas Gonon, Christoph Schwab
Deep ReLU Network Expression Rates for Option Prices in high-dimensional, exponential Lévy models. CoRR (2021)
Lukas Gonon
Random feature neural networks learn Black-Scholes type PDEs without curse of dimensionality. CoRR (2021)
Lukas Gonon, Juan-Pablo Ortega
Fading memory echo state networks are universal. Neural Networks 138 (2021)
Lukas Gonon, Christoph Schwab
Deep ReLU Neural Network Approximation for Stochastic Differential Equations with Jumps. CoRR (2021)
Christian Beck, Lukas Gonon, Arnulf Jentzen
Overcoming the curse of dimensionality in the numerical approximation of high-dimensional semilinear elliptic partial differential equations. CoRR (2020)
Christa Cuchiero, Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega, Josef Teichmann
Discrete-time signatures and randomness in reservoir computing. CoRR (2020)
Aritz Bercher, Lukas Gonon, Arnulf Jentzen, Diyora Salimova
Weak error analysis for stochastic gradient descent optimization algorithms. CoRR (2020)
Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega
Approximation Bounds for Random Neural Networks and Reservoir Systems. CoRR (2020)
Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega
Memory and forecasting capacities of nonlinear recurrent networks. CoRR (2020)
Lukas Gonon, Juan-Pablo Ortega
Reservoir Computing Universality With Stochastic Inputs. IEEE Trans. Neural Networks Learn. Syst. 31 (1) (2020)
Lukas Gonon, Juan-Pablo Ortega
Fading memory echo state networks are universal. CoRR (2020)
Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega
Risk bounds for reservoir computing. CoRR (2019)
Lukas Gonon, Philipp Grohs, Arnulf Jentzen, David Kofler, David Siska
Uniform error estimates for artificial neural network approximations for heat equations. CoRR (2019)
Lukas Gonon, Juan-Pablo Ortega
Reservoir Computing Universality With Stochastic Inputs. CoRR (2018)
Amanda Prorok, Lukas Gonon, Alcherio Martinoli
Online model estimation of ultra-wideband TDOA measurements for mobile robot localization. ICRA (2012)