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Scott Lundberg
Scott Lundberg
Microsoft Research
Geverifieerd e-mailadres voor microsoft.com - Homepage
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A unified approach to interpreting model predictions
SM Lundberg, SI Lee
Advances in neural information processing systems 30, 2017
99732017
From local explanations to global understanding with explainable AI for trees
SM Lundberg, G Erion, H Chen, A DeGrave, JM Prutkin, B Nair, R Katz, ...
Nature machine intelligence 2 (1), 56-67, 2020
18822020
Consistent individualized feature attribution for tree ensembles
SM Lundberg, GG Erion, SI Lee
arXiv preprint arXiv:1802.03888, 2018
10122018
Explainable machine-learning predictions for the prevention of hypoxaemia during surgery
SM Lundberg, B Nair, MS Vavilala, M Horibe, MJ Eisses, T Adams, ...
Nature biomedical engineering 2 (10), 749-760, 2018
8032018
Explainable AI for trees: From local explanations to global understanding
SM Lundberg, G Erion, H Chen, A DeGrave, JM Prutkin, B Nair, R Katz, ...
arXiv preprint arXiv:1905.04610, 2019
2332019
A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia
SI Lee, S Celik, BA Logsdon, SM Lundberg, TJ Martins, VG Oehler, ...
Nature communications 9 (1), 1-13, 2018
1912018
An unexpected unity among methods for interpreting model predictions
S Lundberg, SI Lee
arXiv preprint arXiv:1611.07478, 2016
1132016
Visualizing the impact of feature attribution baselines
P Sturmfels, S Lundberg, SI Lee
Distill 5 (1), e22, 2020
1112020
Understanding global feature contributions with additive importance measures
I Covert, SM Lundberg, SI Lee
Advances in Neural Information Processing Systems 33, 17212-17223, 2020
1102020
Consistent feature attribution for tree ensembles
SM Lundberg, SI Lee
arXiv preprint arXiv:1706.06060, 2017
892017
True to the Model or True to the Data?
H Chen, JD Janizek, S Lundberg, SI Lee
arXiv preprint arXiv:2006.16234, 2020
822020
A unified approach to interpreting model predictions. arXiv 2017
S Lundberg, SI Lee
arXiv preprint arXiv:1705.07874, 2022
812022
Explaining by Removing: A Unified Framework for Model Explanation.
I Covert, SM Lundberg, SI Lee
J. Mach. Learn. Res. 22, 209:1-209:90, 2021
792021
Explaining models by propagating Shapley values of local components
H Chen, S Lundberg, SI Lee
Explainable AI in Healthcare and Medicine, 261-270, 2021
702021
Improving performance of deep learning models with axiomatic attribution priors and expected gradients
G Erion, JD Janizek, P Sturmfels, SM Lundberg, SI Lee
Nature machine intelligence 3 (7), 620-631, 2021
652021
Learning explainable models using attribution priors
G Erion, JD Janizek, P Sturmfels, SM Lundberg, SI Lee
642019
Shapley flow: A graph-based approach to interpreting model predictions
J Wang, J Wiens, S Lundberg
International Conference on Artificial Intelligence and Statistics, 721-729, 2021
472021
Checkpoint ensembles: Ensemble methods from a single training process
H Chen, S Lundberg, SI Lee
arXiv preprint arXiv:1710.03282, 2017
432017
ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data
SM Lundberg, WB Tu, B Raught, LZ Penn, MM Hoffman, SI Lee
Genome biology 17 (1), 1-19, 2016
362016
Intelligible and explainable machine learning: best practices and practical challenges
R Caruana, S Lundberg, MT Ribeiro, H Nori, S Jenkins
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020
262020
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