We propose a robust multitask kernel ridge regression procedure that combines pooled estimation, task-specific residual correction, filtering-based robust aggregation, and adaptive personalization, with single-task estimators serving as safety anchors.

We establish finite-sample convergence rates that adapt to heterogeneity among the related tasks, while ensuring that every task retains, up to logarithmic factors, the convergence rate attainable by fitting that task separately. In particular, when all tasks share the same covariate distribution, we derive minimax lower bounds and show that our convergence rates match them up to logarithmic factors. Numerical experiments demonstrate substantial gains from information sharing among related tasks while maintaining reliable task-specific performance in the presence of unrelated tasks and covariate shift. This talk is based on a joint work with Jinyuan Chang, Yue Wang and Myeonghun Yu.

Bio: I am an Associate Professor in the Department of Information and Decision Sciences at the University of Illinois Chicago. Before joining UIC in 2023, I was an Associate Professor in the Department of Mathematics at UC San Diego. My research centers on high-dimensional and robust statistics, quantile regression, and nonparametric methods. I serve as an Associate Editor for the Journal of the Royal Statistical Society: Series B, Journal of the American Statistical Association, Statistica Sinica, and The Annals of Applied Probability.