Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process
Models

Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models

24 May 2023

Krikamol Muandet

Dino Sejdinovic

Papers citing "Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models"

8 / 8 papers shown

Title
Comparative Explanations: Explanation Guided Decision Making for Human-in-the-Loop Preference Selection Tanmay Chakraborty Christian Wirth Christin Seifert 28 0 0 01 Apr 2025
All You Need for Counterfactual Explainability Is Principled and Reliable Estimate of Aleatoric and Epistemic Uncertainty Kacper Sokol Eyke Hüllermeier 53 2 0 24 Feb 2025
A Gaussian Process Model for Ordinal Data with Applications to Chemoinformatics Arron Gosnell Evangelos A. Evangelou 31 0 0 16 May 2024
Explainable Learning with Gaussian Processes Kurt Butler Guanchao Feng P. Djuric 39 1 0 11 Mar 2024
Explaining Probabilistic Models with Distributional Values Luca Franceschi Michele Donini Cédric Archambeau Matthias Seeger FAtt 37 2 0 15 Feb 2024
Looping in the Human Collaborative and Explainable Bayesian Optimization Masaki Adachi Brady Planden David A. Howey Michael A. Osborne Sebastian Orbell Natalia Ares Krikamol Maundet Siu Lun Chau 28 14 0 26 Oct 2023
Boundary-Aware Uncertainty for Feature Attribution Explainers Davin Hill A. Masoomi Max Torop S. Ghimire Jennifer Dy FAtt 65 3 0 05 Oct 2022
FastSHAP: Real-Time Shapley Value Estimation N. Jethani Mukund Sudarshan Ian Covert Su-In Lee Rajesh Ranganath TDI FAtt 67 123 0 15 Jul 2021