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Found 13 papers, 1 papers with code
Actor critic learning algorithms for mean-field control with moment neural networks
We develop a new policy gradient and actor-critic algorithm for solving mean-field control problems within a continuous time reinforcement learning setting.
A Common Shock Model for multidimensional electricity intraday price modelling with application to battery valuation
In this paper, we propose a multidimensional statistical model of intraday electricity prices at the scale of the trading session, which allows all products to be simulated simultaneously.
Quantile and moment neural networks for learning functionals of distributions
We study news neural networks to approximate function of distributions in a probability space.
Mean-field neural networks-based algorithms for McKean-Vlasov control problems *
This paper is devoted to the numerical resolution of McKean-Vlasov control problems via the class of mean-field neural networks introduced in our companion paper [25] in order to learn the solution on the Wasserstein space.
Mean-field neural networks: learning mappings on Wasserstein space
We study the machine learning task for models with operators mapping between the Wasserstein space of probability measures and a space of functions, like e. g. in mean-field games/control problems.
Neural networks-based algorithms for stochastic control and PDEs in finance
This paper presents machine learning techniques and deep reinforcement learningbased algorithms for the efficient resolution of nonlinear partial differential equations and dynamic optimization problems arising in investment decisions and derivative pricing in financial engineering.
Optimization and Control Computational Finance
Deep learning for efficient frontier calculation in finance
We propose deep neural network algorithms to calculate efficient frontier in some Mean-Variance and Mean-CVaR portfolio optimization problems.
Incentives, lockdown, and testing: from Thucydides's analysis to the COVID-19 pandemic
In terms of technical results, we demonstrate the optimal form of the tax, indexed on the proportion of infected individuals, as well as the optimal effort of the population, namely the transmission rate chosen in response to this tax.
Discretization and Machine Learning Approximation of BSDEs with a Constraint on the Gains-Process
We then focus on the approximation of the discretely constrained BSDE.
Neural networks-based backward scheme for fully nonlinear PDEs
We propose a numerical method for solving high dimensional fully nonlinear partial differential equations (PDEs).
Risk management with machine-learning-based algorithms
We propose some machine-learning-based algorithms to solve hedging problems in incomplete markets.
Deep backward schemes for high-dimensional nonlinear PDEs
We analyze the convergence of the deep learning schemes and provide error estimates in terms of the universal approximation of neural networks.
Machine Learning for semi linear PDEs
Recent machine learning algorithms dedicated to solving semi-linear PDEs are improved by using different neural network architectures and different parameterizations.
