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         Department of Systems Engineering and Engineering Management

                             The Chinese University of Hong Kong

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Date: 16:30 pm - 17:30 pm on 20 November (Thursday)

Venue: ERB 513, The Chinese University of Hong Kong

Title: Recent Advances in Game-Theoretic Feature Attributions for Kernel

Methods and Gaussian Processes

Speaker: Siu Lun Chau, College of Computing and Data Science, Nanyang

Technological University





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.



Bio:

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.