66 citations · 375 across the 33 of their papers we have counts for
5 papers · 1 filter
Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories
Zixuan Zhang, Minshuo Chen, Mengdi Wang +2
Existing theories on deep nonparametric regression have shown that when the input data lie on a low-dimensional manifold, deep neural networks can adapt to the intrinsic data struc…
High Dimensional Binary Classification under Label Shift: Phase Transition and Regularization
Jiahui Cheng, Minshuo Chen, Hao Liu +2
Label Shift has been widely believed to be harmful to the generalization performance of machine learning models. Researchers have proposed many approaches to mitigate the impact of…
Doubly Robust Off-Policy Learning on Low-Dimensional Manifolds by Deep Neural Networks
Minshuo Chen, Hao Liu, Wenjing Liao +1
Causal inference explores the causation between actions and the consequent rewards on a covariate set. Recently deep learning has achieved a remarkable performance in causal infere…
Distribution Approximation and Statistical Estimation Guarantees of Generative Adversarial Networks
Minshuo Chen, Wenjing Liao, Hongyuan Zha +1
Generative Adversarial Networks (GANs) have achieved a great success in unsupervised learning. Despite its remarkable empirical performance, there are limited theoretical studies o…
Nonparametric Regression on Low-Dimensional Manifolds using Deep ReLU Networks : Function Approximation and Statistical Recovery
Minshuo Chen, Haoming Jiang, Wenjing Liao +1
Real world data often exhibit low-dimensional geometric structures, and can be viewed as samples near a low-dimensional manifold. This paper studies nonparametric regression of Höl…