| A unified approach to interpreting model predictions S Lundberg arXiv preprint arXiv:1705.07874, 2017 | 25868 | 2017 |
| 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 | 4748 | 2020 |
| Sparks of artificial general intelligence: Early experiments with gpt-4 S Bubeck, V Chandrasekaran, R Eldan, J Gehrke, E Horvitz, E Kamar, ... arXiv preprint arXiv:2303.12712, 2023 | 2741 | 2023 |
| Consistent individualized feature attribution for tree ensembles SM Lundberg, GG Erion, SI Lee arXiv preprint arXiv:1802.03888, 2018 | 1992 | 2018 |
| 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 | 1505 | 2018 |
| 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 | 377 | 2019 |
| 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), 42, 2018 | 345 | 2018 |
| Understanding global feature contributions with additive importance measures I Covert, SM Lundberg, SI Lee Advances in Neural Information Processing Systems 33, 17212-17223, 2020 | 327 | 2020 |
| Sparks of artificial general intelligence: Early experiments with GPT-4. arXiv S Bubeck, V Chandrasekaran, R Eldan, J Gehrke, E Horvitz, E Kamar, ... arXiv preprint arXiv:2303.12712, 2023 | 291 | 2023 |
| Explaining by removing: A unified framework for model explanation I Covert, S Lundberg, SI Lee Journal of Machine Learning Research 22 (209), 1-90, 2021 | 253 | 2021 |
| Visualizing the impact of feature attribution baselines P Sturmfels, S Lundberg, SI Lee Distill 5 (1), e22, 2020 | 228 | 2020 |
| 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 | 219 | 2021 |
| Consistent feature attribution for tree ensembles SM Lundberg, SI Lee arXiv preprint arXiv:1706.06060, 2017 | 174 | 2017 |
| True to the model or true to the data? H Chen, JD Janizek, S Lundberg, SI Lee arXiv preprint arXiv:2006.16234, 2020 | 165 | 2020 |
| An unexpected unity among methods for interpreting model predictions S Lundberg, SI Lee arXiv preprint arXiv:1611.07478, 2016 | 159 | 2016 |
| Art: Automatic multi-step reasoning and tool-use for large language models B Paranjape, S Lundberg, S Singh, H Hajishirzi, L Zettlemoyer, ... arXiv preprint arXiv:2303.09014, 2023 | 140 | 2023 |
| Explaining models by propagating Shapley values of local components H Chen, S Lundberg, SI Lee Explainable AI in Healthcare and Medicine: Building a Culture of …, 2021 | 128 | 2021 |
| Algorithms to estimate Shapley value feature attributions H Chen, IC Covert, SM Lundberg, SI Lee Nature Machine Intelligence 5 (6), 590-601, 2023 | 125 | 2023 |
| Consistent individualized feature attribution for tree ensembles. arXiv 2018 SM Lundberg, GG Erion, SI Lee arXiv preprint arXiv:1802.03888 10, 1802 | 123 | 1802 |
| 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 | 112 | 2021 |
Su-In LeeComputer Science & Engineering, University of WashingtonPotvrđena adresa e-pošte na cs.washington.edu