Robust multi-task boosting using clustering and local ensembling

Multi-Task Learning (MTL) aims to boost predictive performance by sharing information across related tasks, however, conventional methods often suffer from negative transfer when unrelated or noisy tasks are forced to share representations. We propose Robust Multi-Task Boosting using Clustering and Local Ensembling (RMB-CLE), a principled MTL framework that integrates error-based task clustering with local ensembling. Unlike prior work that assumes fixed clusters or hand-crafted similarity metrics, RMB-CLE derives inter-task similarity directly from cross-task errors , which admits a principled characterization of cross-task risk in terms of functional mismatch and uncertainty, providing a theoretically grounded mechanism to prevent negative transfer. Tasks are grouped adaptively via agglomerative clustering, and within each cluster, a local ensemble enables robust knowledge sharing while preserving task-specific patterns. Experiments show that RMB-CLE recovers ground-truth clusters in synthetic data and consistently outperforms multi-task, single-task, and pooling-based ensemble methods across diverse synthetic and real-world tabular benchmarks. These results demonstrate that RMB-CLE provides an effective boosting-based framework for robust multi-task learning on structured tabular datasets, where task heterogeneity and negative transfer are central challenges.