971 citations · 3k across the 74 of their papers we have counts for
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RKHS-SHAP: Shapley Values for Kernel Methods
Siu Lun Chau, Robert Hu, Javier Gonzalez +1
Feature attribution for kernel methods is often heuristic and not individualised for each prediction. To address this, we turn to the concept of Shapley values~(SV), a coalition ga…
BayesIMP: Uncertainty Quantification for Causal Data Fusion
Siu Lun Chau, Jean-François Ton, Javier González +2
While causal models are becoming one of the mainstays of machine learning, the problem of uncertainty quantification in causal inference remains challenging. In this paper, we stud…
Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes
Veit Wild, Motonobu Kanagawa, Dino Sejdinovic
We investigate the connections between sparse approximation methods for making kernel methods and Gaussian processes (GPs) scalable to large-scale data, focusing on the Nyström met…
Survival Regression with Proper Scoring Rules and Monotonic Neural Networks
David Rindt, Robert Hu, David Steinsaltz +1
We consider frequently used scoring rules for right-censored survival regression models such as time-dependent concordance, survival-CRPS, integrated Brier score and integrated bin…