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Found 6 papers, 1 papers with code
Predicting Daily Trading Volume via Various Hidden States
Predicting intraday trading volume plays an important role in trading alpha research.
Neural Monge Map estimation and its applications
Monge map refers to the optimal transport map between two probability distributions and provides a principled approach to transform one distribution to another.
Mean Field Game GAN
We propose a novel mean field games (MFGs) based GAN(generative adversarial network) framework.
Learning High Dimensional Wasserstein Geodesics
We propose a new formulation and learning strategy for computing the Wasserstein geodesic between two probability distributions in high dimensions.
Learning Cost Functions for Optimal Transport
Inverse optimal transport (OT) refers to the problem of learning the cost function for OT from observed transport plan or its samples.
Learning Stochastic Behaviour from Aggregate Data
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 time may not be observed at the next time point, or the identity of individual is unavailable.
