Robust multi-task boosting using clustering and local ensembling

2026-02-15

AI Summary

This work proposes an error-driven adaptive multi-task boosting framework to mitigate negative transfer caused by forcing unrelated or noisy tasks to share representations. The approach introduces a novel definition of task similarity based on cross-task error decomposition, enabling dynamic task grouping via agglomerative clustering while preserving task-specific patterns through local ensembles. Grounded in theoretical analysis, the method achieves robust adaptive clustering and ensemble learning. Experiments on both synthetic and real-world datasets demonstrate its ability to accurately recover true task clusters and consistently outperform single-task learning, conventional multi-task approaches, and pooled ensemble methods.

Technology Category

• Machine Learning: Ensemble Methods
• Multiagent Systems: Adversarial Agents
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Multi-Task Learning (MTL) aims to boost predictive performance by sharing information across related tasks, yet 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 admit a risk decomposition into functional mismatch and irreducible noise, 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 real-world and synthetic benchmarks. These results demonstrate that RMB-CLE is not merely a combination of clustering and boosting but a general and scalable framework that establishes a new basis for robust multi-task learning.

Problem

• Multi-Task Learning
• Negative Transfer
• Task Clustering
• Robust Learning
• Ensemble Methods

Innovation

• multi-task learning
• negative transfer
• task clustering
• local ensembling
• error-based similarity