Emmanuel Gobet

Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations

A Proof that Artificial Neural Networks Overcome the Curse of Dimensionality in the Numerical Approximation of Black–Scholes Partial Differential Equations

An Energy-Efficient Transaction Model for the Blockchain-Enabled Internet of Vehicles (IoV)

Nonlinear principal components and long-run implications of multivariate diffusions

On the approximation of stochastic integrals

A Selective Overview of Nonparametric Methods in Financial Econometrics

Deep Learning-Based Numerical Methods for High-Dimensional Parabolic Partial Differential Equations and Backward Stochastic Differential Equations

Exact and Computationally Efficient Likelihood-Based Estimation for Discretely Observed Diffusion Processes (with Discussion)

Dynamic Portfolio Choice and Risk Aversion

Physics-informed machine learning

Solving high-dimensional partial differential equations using deep learning

A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

DGM: A deep learning algorithm for solving partial differential equations

Machine Learning Approximation Algorithms for High-Dimensional Fully Nonlinear Partial Differential Equations and Second-order Backward Stochastic Differential Equations

The Malliavin Calculus and Related Topics

The Cross‐Section of Volatility and Expected Returns

Linear regression MDP scheme for discrete backward stochastic differential equations under general conditions

Discrete time hedging errors for options with irregular payoffs

Time Dependent Heston Model

Solving BSDE with Adaptive Control Variate

Propriété LAN pour les diffusions ergodiques avec observations discrètes

Stratified Regression Monte-Carlo Scheme for Semilinear PDEs and BSDEs with Large Scale Parallelization on GPUs

Monte-Carlo methods and stochastic processes: from linear to non-linear

Time Dependent Heston Model

Approximation of backward stochastic differential equations using Malliavin weights and least-squares regression

A regression-based Monte Carlo method to solve backward stochastic differential equations

Rate of convergence of an empirical regression method for solving generalized backward stochastic differential equations

Adaptive importance sampling in least-squares Monte Carlo algorithms for backward stochastic differential equations