2 papers
cs.LG2026
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…
cs.LG2026
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…