6 papers
S2MAM: Semi-supervised Meta Additive Model for Robust Estimation and Variable Selection
Xuelin Zhang, Hong Chen, Yingjie Wang +2
Semi-supervised learning with manifold regularization is a classical framework for jointly learning from both labeled and unlabeled data, where the key requirement is that the supp…
Quantifying Multimodal Capabilities: Formal Generalization Guarantees in Pairwise Metric Learning
Richeng Zhou, Xuelin Zhang, Liyuan Liu
Multimodal learning leverages the integration of diverse data modalities to enhance performance in complex tasks. Yet, it frequently encounters incomplete or redundant modality dat…
On the Stability and Generalization of First-order Bilevel Minimax Optimization
Xuelin Zhang, Peipei Yuan
Bilevel optimization and bilevel minimax optimization have recently emerged as unifying frameworks for a range of machine-learning tasks, including hyperparameter optimization and…
Meta Additive Model: Interpretable Sparse Learning With Auto Weighting
Xuelin Zhang, Xinyue Liu, Lingjuan Wu +1
Sparse additive models have attracted much attention in high-dimensional data analysis due to their flexible representation and strong interpretability. However, most existing mode…
Fine-grained Analysis of Stability and Generalization for Stochastic Bilevel Optimization
Xuelin Zhang, Hong Chen, Bin Gu +2
Stochastic bilevel optimization (SBO) has been integrated into many machine learning paradigms recently, including hyperparameter optimization, meta learning, and reinforcement lea…
Beyond False Discovery Rate: A Stepdown Group SLOPE Approach for Grouped Variable Selection
Xuelin Zhang, Jingxuan Liang, Xinyue Liu +2
High-dimensional feature selection is routinely required to balance statistical power with strict control of multiple-error metrics such as the k-Family-Wise Error Rate (k-FWER) an…