4 papers
On the Optimality of Random Features under General Conditions: Misspecified Cases and Robust Learning
Caixing Wang
Random features provide a standard approach to scaling kernel methods to large datasets by approximating the kernel with a finite-dimensional feature map. Existing works of random…
High-Dimensional Differentially Private Quantile Regression: Distributed Estimation and Statistical Inference
Ziliang Shen, Caixing Wang, Shaoli Wang +1
With the development of big data and machine learning, privacy concerns have become increasingly critical, especially when handling heterogeneous datasets containing sensitive pers…
Inertial Quadratic Majorization Minimization with Application to Kernel Regularized Learning
Qiang Heng, Caixing Wang
First-order methods in convex optimization offer low per-iteration cost but often suffer from slow convergence, while second-order methods achieve fast local convergence at the exp…
Distributed High-Dimensional Quantile Regression: Estimation Efficiency and Support Recovery
Caixing Wang, Ziliang Shen
In this paper, we focus on distributed estimation and support recovery for high-dimensional linear quantile regression. Quantile regression is a popular alternative tool to the lea…