arXiv stat.ML
· Papers
Contaminated Multi-task Learning with Heterogeneity: Fundamental Limits and Optimal Algorithms
arXiv:2607.02681v1 Announce Type: new Abstract: Integrating information across related tasks can improve estimation and prediction in transfer, multi-task, and federated learning, but contamination and heterogeneity make robust borrowing challenging. We study a contaminated multi-task empirical risk minimization (ERM)