4 citations · 9 across the 6 of their papers we have counts for
6 papers
Directed Chain Generative Adversarial Networks
Ming Min, Ruimeng Hu, Tomoyuki Ichiba
Real-world data can be multimodal distributed, e.g., data describing the opinion divergence in a community, the interspike interval distribution of neurons, and the oscillators nat…
Deep Learning for Systemic Risk Measures
Yichen Feng, Ming Min, Jean-Pierre Fouque
The aim of this paper is to study a new methodological framework for systemic risk measures by applying deep learning method as a tool to compute the optimal strategy of capital al…
Smoothness of Directed Chain Stochastic Differential Equations
Tomoyuki Ichiba, Ming Min
We study the smoothness of the solution of the directed chain stochastic differential equations, where each process is affected by its neighborhood process in an infinite directed…
Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost
Dan Qiao, Ming Yin, Ming Min +1
We study the problem of reinforcement learning (RL) with low (policy) switching cost - a problem well-motivated by real-life RL applications in which deployments of new policies ar…
Signatured Deep Fictitious Play for Mean Field Games with Common Noise
Ming Min, Ruimeng Hu
Existing deep learning methods for solving mean-field games (MFGs) with common noise fix the sampling common noise paths and then solve the corresponding MFGs. This leads to a nest…
Convolutional Signature for Sequential Data
Ming Min, Tomoyuki Ichiba
Signature is an infinite graded sequence of statistics known to characterize geometric rough paths, which includes the paths with bounded variation. This object has been studied su…