Recent developments in machine learning methods for stochastic control and games
R Hu, M Lauriere - arXiv preprint arXiv:2303.10257, 2023 - arxiv.org
Stochastic optimal control and games have a wide range of applications, from finance and
economics to social sciences, robotics, and energy management. Many real-world …
economics to social sciences, robotics, and energy management. Many real-world …
Latent trajectory learning for limited timestamps under distribution shift over time
Distribution shifts over time are common in real-world machine-learning applications. This
scenario is formulated as Evolving Domain Generalization (EDG), where models aim to …
scenario is formulated as Evolving Domain Generalization (EDG), where models aim to …
[图书][B] Topics in Signature, Directed Chain SDEs and Applications in Machine Learning
M Min - 2023 - search.proquest.com
Stochastic analysis, stochastic processes and machine learning of dynamical systems have
depicted strong connection in many aspects. This thesis aims to study such relationship from …
depicted strong connection in many aspects. This thesis aims to study such relationship from …
Coherent Time Series Generative Models Using Dynamic Mode Decomposition
K Nitjaphanich - 2024 - search.proquest.com
It is a well-known problem for generative models such as Generative Adversarial Networks
(GANs) and Variational Autoencoders (VAEs) to sometimes fail to generate the full extent of …
(GANs) and Variational Autoencoders (VAEs) to sometimes fail to generate the full extent of …