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.