Zobrazeno 1 - 10
of 1 153
pro vyhledávání: '"Warin, A. P."'
Autor:
Warin, Xavier
A new Kolmogorov-Arnold network (KAN) is proposed to approximate potentially irregular functions in high dimension. We show that it outperforms multilayer perceptrons in terms of accuracy and converges faster. We also compare it with several proposed
Externí odkaz:
http://arxiv.org/abs/2410.03801
We propose a comprehensive framework for policy gradient methods tailored to continuous time reinforcement learning. This is based on the connection between stochastic control problems and randomised problems, enabling applications across various cla
Externí odkaz:
http://arxiv.org/abs/2404.17939
Autor:
Bokanowski, Olivier, Warin, Xavier
We study deterministic optimal control problems for differential games with finite horizon. We propose new approximations of the strategies in feedback form, and show error estimates and a convergence result of the value in some weak sense for one of
Externí odkaz:
http://arxiv.org/abs/2402.02792
In this paper, we introduce various machine learning solvers for (coupled) forward-backward systems of stochastic differential equations (FBSDEs) driven by a Brownian motion and a Poisson random measure. We provide a rigorous comparison of the differ
Externí odkaz:
http://arxiv.org/abs/2401.03245
Autor:
Pham, Huyên, Warin, Xavier
We develop a new policy gradient and actor-critic algorithm for solving mean-field control problems within a continuous time reinforcement learning setting. Our approach leverages a gradient-based representation of the value function, employing param
Externí odkaz:
http://arxiv.org/abs/2309.04317
Autor:
Deschatre, Thomas, Warin, Xavier
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. This model, based on Poisson measures and inspired by the Com
Externí odkaz:
http://arxiv.org/abs/2307.16619
Autor:
Warin, Xavier
We study news neural networks to approximate function of distributions in a probability space. Two classes of neural networks based on quantile and moment approximation are proposed to learn these functions and are theoretically supported by universa
Externí odkaz:
http://arxiv.org/abs/2303.11060
Autor:
Pham, Huyên, Warin, Xavier
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. We propose several algorit
Externí odkaz:
http://arxiv.org/abs/2212.11518
Autor:
M. Guinebretière, L. Warin, J.P. Moysan, B. Méda, F. Mocz, E. Le Bihan-Duval, R. Thomas, A. Keita, S. Mignon-Grasteau
Publikováno v:
Poultry Science, Vol 103, Iss 9, Pp 103993- (2024)
ABSTRACT: Conventional broiler production needs to evolve towards more animal-friendly production systems in order to meet increasing consumer concerns regarding animal welfare. Genetics and stocking density are 2 of the most promising leads to make
Externí odkaz:
https://doaj.org/article/d1475c557e58413abb781b7dc8c16213
Autor:
Pham, Huyên, Warin, Xavier
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. Two classes of neural networks, based on bin density
Externí odkaz:
http://arxiv.org/abs/2210.15179