16 citations · 87 across the 30 of their papers we have counts for
7 papers · 1 filter
Wasserstein Generative Learning of Conditional Distribution
Shiao Liu, Xingyu Zhou, Yuling Jiao +1
Conditional distribution is a fundamental quantity for describing the relationship between a response and a predictor. We propose a Wasserstein generative approach to learning a co…
Non-Asymptotic Error Bounds for Bidirectional GANs
Shiao Liu, Yunfei Yang, Jian Huang +2
We derive nearly sharp bounds for the bidirectional GAN (BiGAN) estimation error under the Dudley distance between the latent joint distribution and the data joint distribution wit…
An error analysis of generative adversarial networks for learning distributions
Jian Huang, Yuling Jiao, Zhen Li +3
This paper studies how well generative adversarial networks (GANs) learn probability distributions from finite samples. Our main results establish the convergence rates of GANs und…
Non-asymptotic Excess Risk Bounds for Classification with Deep Convolutional Neural Networks
Guohao Shen, Yuling Jiao, Yuanyuan Lin +1
In this paper, we consider the problem of binary classification with a class of general deep convolutional neural networks, which includes fully-connected neural networks and fully…
Generative Learning With Euler Particle Transport
Yuan Gao, Jian Huang, Yuling Jiao +3
We propose an Euler particle transport (EPT) approach for generative learning. The proposed approach is motivated by the problem of finding an optimal transport map from a referenc…
Deep Dimension Reduction for Supervised Representation Learning
Jian Huang, Yuling Jiao, Xu Liao +2
The goal of supervised representation learning is to construct effective data representations for prediction. Among all the characteristics of an ideal nonparametric representation…
Learning Implicit Generative Models with Theoretical Guarantees
Yuan Gao, Jian Huang, Yuling Jiao +1
We propose a \textbf{uni}fied \textbf{f}ramework for \textbf{i}mplicit \textbf{ge}nerative \textbf{m}odeling (UnifiGem) with theoretical guarantees by integrating approaches from o…