6 citations · 22 across the 7 of their papers we have counts for
7 papers
High-dimensional Optimal Density Control with Wasserstein Metric Matching
Shaojun Ma, Mengxue Hou, Xiaojing Ye +1
We present a novel computational framework for density control in high-dimensional state spaces. The considered dynamical system consists of a large number of indistinguishable age…
Predicting Daily Trading Volume via Various Hidden States
Shaojun Ma, Pengcheng Li
Predicting intraday trading volume plays an important role in trading alpha research. Existing methods such as rolling means(RM) and a two-states based Kalman Filtering method have…
Neural Monge Map estimation and its applications
Jiaojiao Fan, Shu Liu, Shaojun Ma +2
Monge map refers to the optimal transport map between two probability distributions and provides a principled approach to transform one distribution to another. Neural network base…
Mean Field Game GAN
Shaojun Ma, Haomin Zhou, Hongyuan Zha
We propose a novel mean field games (MFGs) based GAN(generative adversarial network) framework. To be specific, we utilize the Hopf formula in density space to rewrite MFGs as a pr…
Learning High Dimensional Wasserstein Geodesics
Shu Liu, Shaojun Ma, Yongxin Chen +2
We propose a new formulation and learning strategy for computing the Wasserstein geodesic between two probability distributions in high dimensions. By applying the method of Lagran…
Learning Cost Functions for Optimal Transport
Shaojun Ma, Haodong Sun, Xiaojing Ye +2
Inverse optimal transport (OT) refers to the problem of learning the cost function for OT from observed transport plan or its samples. In this paper, we derive an unconstrained con…
Learning Stochastic Behaviour from Aggregate Data
Shaojun Ma, Shu Liu, Hongyuan Zha +1
Learning nonlinear dynamics from aggregate data is a challenging problem because the full trajectory of each individual is not available, namely, the individual observed at one tim…