4 papers
Reducing Learner Redundancy in Boosting via Residual Orthogonalization
Ye Su, Jipeng Guo, Yong Liu +5
While sequential residual fitting is the bedrock of standard boosting frameworks, it inherently breeds learner redundancy by repeatedly revisiting correlated error components. To a…
ITBoost: Information-Theoretic Trust for Robust Boosting
Ye Su, Longlong Zhao, Diego Garcia-Gil +4
Gradient boosting remains a strong and widely used method for tabular data learning, but its performance often degrades when training labels are noisy. This behavior is largely rel…
Exact Finite-Sample Variance Decomposition of Subagging: A Spectral Filtering Perspective
Ye Su, Mingrui Ye, Yining Wang +2
Standard resampling ratios (e.g., ) are widely used as default baselines in ensemble learning for three decades. However, how these ratios interact with a base lea…
Hybrid-Collaborative Augmentation and Contrastive Sample Adaptive-Differential Awareness for Robust Attributed Graph Clustering
Tianxiang Zhao, Youqing Wang, Jinlu Wang +4
Due to its powerful capability of self-supervised representation learning and clustering, contrastive attributed graph clustering (CAGC) has achieved great success, which mainly de…