4 papers
Reducing Learner Redundancy in Boosting via Residual Orthogonalization
Ye Su, Jipeng Guo, Xin Xu +4
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 lear…
BamaER: A Behavior-Aware Memory-Augmented Model for Exercise Recommendation
Qing Yang, Yuhao Jiang, Rui Wang +6
Exercise recommendation focuses on personalized exercise selection conditioned on students' learning history, personal interests, and other individualized characteristics. Despite…