9 citations · 10 across the 6 of their papers we have counts for
8 papers
Deep Generators on Commodity Markets; application to Deep Hedging
Nicolas Boursin, Carl Remlinger, Joseph Mikael +1
Driven by the good results obtained in computer vision, deep generative methods for time series have been the subject of particular attention in recent years, particularly from the…
On the challenges of using D-Wave computers to sample Boltzmann Random Variables
Thomas Pochart, Paulin Jacquot, Joseph Mikael
Sampling random variables following a Boltzmann distribution is an NP-hard problem involved in various applications such as training of \textit{Boltzmann machines}, a specific kind…
Expert Aggregation for Financial Forecasting
Carl Remlinger, Brière Marie, Alasseur Clémence +1
Machine learning algorithms dedicated to financial time series forecasting have gained a lot of interest. But choosing between several algorithms can be challenging, as their estim…
Conditional Loss and Deep Euler Scheme for Time Series Generation
Carl Remlinger, Joseph Mikael, Romuald Elie
We introduce three new generative models for time series that are based on Euler discretization of Stochastic Differential Equations (SDEs) and Wasserstein metrics. Two of these me…
Deep combinatorial optimisation for optimal stopping time problems : application to swing options pricing
Thomas Deschatre, Joseph Mikael
A new method for stochastic control based on neural networks and using randomisation of discrete random variables is proposed and applied to optimal stopping time problems. The met…
Numerical resolution of McKean-Vlasov FBSDEs using neural networks
Maximilien Germain, Joseph Mikael, Xavier Warin
We propose several algorithms to solve McKean-Vlasov Forward Backward Stochastic Differential Equations. Our schemes rely on the approximating power of neural networks to estimate…
Risk management with machine-learning-based algorithms
Simon Fécamp, Joseph Mikael, Xavier Warin
We propose some machine-learning-based algorithms to solve hedging problems in incomplete markets. Sources of incompleteness cover illiquidity, untradable risk factors, discrete he…
Machine Learning for semi linear PDEs
Quentin Chan-Wai-Nam, Joseph Mikael, Xavier Warin
Recent machine learning algorithms dedicated to solving semi-linear PDEs are improved by using different neural network architectures and different parameterizations. These algorit…