Abstract
Kernel methods and Gaussian processes are powerful nonparametric learning frameworks grounded in positive definite kernels. Yet, their flexible black-box nature often comes at the cost of interpretability. This seminar presents recent advances in game-theoretic feature attribution for kernel methods and Gaussian processes, bridging cooperative game theory with kernel-based learning. I will discuss how these methods offer principled and computationally tractable attributions—reducing the exponential complexity of Shapley value estimation to polynomial time—and how they naturally extend to explain not only predictions, but also distributional discrepancies, dependency measures, and predictive uncertainty.
About the speaker
Siu Lun Chau is an Assistant Professor in Statistical Machine Learning at Nanyang Technological University, Singapore. His research focuses on understanding and addressing epistemic uncertainty in machine learning—how to represent, quantify, propagate, compare, and explain knowledge-level uncertainty in intelligent systems. Before joining NTU, he was a Postdoctoral Researcher at the CISPA Helmholtz Center for Information Security with Dr. Krikamol Muandet and obtained his DPhil in Statistics from the University of Oxford under the supervision of Prof. Dino Sejdinovic. His work has been recognised with the IJAR Young Researcher Award for contributions at the intersection of imprecise probability theory and machine learning.
