3 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…
cs.LG2025
Why Federated Optimization Fails to Achieve Perfect Fitting? A Theoretical Perspective on Client-Side Optima
Zhongxiang Lei, Qi Yang, Ping Qiu +3
Federated optimization is a constrained form of distributed optimization that enables training a global model without directly sharing client data. Although existing algorithms can…