3 papers
stat.ML2026
Renewable Lasso without Batch-Number Constraints: A Gradient-Enhanced Approach
Junzhuo Gao, Ling Peng, Xu Guo +1
We study online estimation for high-dimensional generalized linear models with streaming data. First, for the non-distributed setting, we propose a gradient-enhanced surrogate loss…
stat.ME2025
Statistical inference for high-dimensional convoluted rank regression
Leheng Cai, Xu Guo, Heng Lian +1
High-dimensional penalized rank regression is a powerful tool for modeling high-dimensional data due to its robustness and estimation efficiency. However, the non-smoothness of the…
stat.ME2025
High-dimensional inference for single-index model with latent factors
Yanmei Shi, Meiling Hao, Yanlin Tang +2
Models with latent factors recently attract a lot of attention. However, most investigations focus on linear regression models and thus cannot capture nonlinearity. To address this…