7 citations · 8 across the 3 of their papers we have counts for
4 papers
ConquerNet: Convolution-Smoothed Quantile ReLU Neural Networks with Minimax Guarantees
Tianpai Luo, Fangwei Wu, Weichi Wu
Quantile regression is a fundamental tool for distributional learning but poses significant optimization challenges for deep models due to the non-smoothness of the pinball loss. W…
Complex trend inference for high-dimensional piecewise locally stationary time series
Lujia Bai, David Veitch, Weichi Wu +2
This paper studies high-dimensional trend inference for piecewise smooth signals under nonstationary noise and asynchronous structural breaks by first detecting asynchronous change…
Adaptive Estimation for Non-stationary Factor Models And A Test for Static Factor Loadings
Weichi Wu, Zhou Zhou
This paper considers the estimation and testing of a class of locally stationary time series factor models with evolutionary temporal dynamics. In particular, the entries and the d…
Multiscale Jump Testing and Estimation Under Complex Temporal Dynamics
Weichi Wu, Zhou Zhou
We consider the problem of detecting jumps in an otherwise smoothly evolving trend whilst the covariance and higher-order structures of the system can experience both smooth and ab…