36 citations · 70 across the 4 of their papers we have counts for
6 papers
Quadrature-TreeSHAP: Depth-Independent TreeSHAP and Shapley Interactions
Ron Wettenstein, Rory Mitchell, Peng Yu
Shapley values are a standard tool for explaining predictions of tree ensembles, with Path-Dependent SHAP being the most widely used variant. Despite substantial progress, existing…
Bandwidth-Optimal Random Shuffling for GPUs
Rory Mitchell, Daniel Stokes, Eibe Frank +1
Linear-time algorithms that are traditionally used to shuffle data on CPUs, such as the method of Fisher-Yates, are not well suited to implementation on GPUs due to inherent sequen…
Sampling Permutations for Shapley Value Estimation
Rory Mitchell, Joshua Cooper, Eibe Frank +1
Game-theoretic attribution techniques based on Shapley values are used to interpret black-box machine learning models, but their exact calculation is generally NP-hard, requiring a…
GPUTreeShap: Massively Parallel Exact Calculation of SHAP Scores for Tree Ensembles
Rory Mitchell, Eibe Frank, Geoffrey Holmes
SHAP (SHapley Additive exPlanation) values provide a game theoretic interpretation of the predictions of machine learning models based on Shapley values. While exact calculation of…
Adaptive XGBoost for Evolving Data Streams
Jacob Montiel, Rory Mitchell, Eibe Frank +3
Boosting is an ensemble method that combines base models in a sequential manner to achieve high predictive accuracy. A popular learning algorithm based on this ensemble method is e…
XGBoost: Scalable GPU Accelerated Learning
Rory Mitchell, Andrey Adinets, Thejaswi Rao +1
We describe the multi-GPU gradient boosting algorithm implemented in the XGBoost library (https://github.com/dmlc/xgboost). Our algorithm allows fast, scalable training on multi-GP…