| A unified approach to interpreting model predictions S Lundberg, SI Lee NeurIPS (arXiv preprint arXiv:1705.07874), 2017 | 26677 | 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 | 4904 | 2020 |
| Consistent individualized feature attribution for tree ensembles SM Lundberg, GG Erion, SI Lee arXiv preprint arXiv:1802.03888, 2018 | 2180 | 2018 |
| Sequencing of Aspergillus nidulans and comparative analysis with A. fumigatus and A. oryzae JE Galagan, SE Calvo, C Cuomo, LJ Ma, JR Wortman, S Batzoglou, ... Nature 438 (7071), 1105-1115, 2005 | 1627 | 2005 |
| 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 | 1538 | 2018 |
| Massively parallel functional dissection of mammalian enhancers in vivo RP Patwardhan, JB Hiatt, DM Witten, MJ Kim, RP Smith, D May, C Lee, ... Nature biotechnology 30 (3), 265-270, 2012 | 623 | 2012 |
| AI for radiographic COVID-19 detection selects shortcuts over signal AJ DeGrave, JD Janizek, SI Lee Nature Machine Intelligence 3 (7), 610-619, 2021 | 584 | 2021 |
| Efficient l~ 1 regularized logistic regression SI Lee, H Lee, P Abbeel, AY Ng Aaai 6, 401-408, 2006 | 550 | 2006 |
| Learning generative models for protein fold families S Balakrishnan, H Kamisetty, JG Carbonell, SI Lee, CJ Langmead Proteins: Structure, Function, and Bioinformatics 79 (4), 1061-1078, 2011 | 387 | 2011 |
| 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 | 384 | 2019 |
| Application of independent component analysis to microarrays SI Lee, S Batzoglou Genome biology 4, 1-21, 2003 | 365 | 2003 |
| Understanding global feature contributions with additive importance measures I Covert, SM Lundberg, SI Lee Advances in Neural Information Processing Systems 33, 17212-17223, 2020 | 364 | 2020 |
| 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 | 351 | 2018 |
| Efficient Structure Learning of Markov Networks using -Regularization SI Lee, V Ganapathi, D Koller Advances in neural Information processing systems, 2006 | 311 | 2006 |
| 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 | 294* | 2021 |
| The proteomic landscape of triple-negative breast cancer RT Lawrence, EM Perez, D Hernández, CP Miller, KM Haas, HY Irie, ... Cell reports 11 (4), 630-644, 2015 | 250 | 2015 |
| Node-based learning of multiple Gaussian graphical models K Mohan, P London, M Fazel, D Witten, SI Lee The Journal of Machine Learning Research 15 (1), 445-488, 2014 | 234 | 2014 |
| 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 | 229 | 2021 |
| Visualizing the impact of feature attribution baselines P Sturmfels, S Lundberg, SI Lee Distill 5 (1), e22, 2020 | 229 | 2020 |
| Learning a prior on regulatory potential from eQTL data SI Lee, AM Dudley, D Drubin, PA Silver, NJ Krogan, D Pe'er, D Koller PLoS genetics 5 (1), e1000358, 2009 | 229 | 2009 |
Scott LundbergGoogle DeepMindVerified email at google.com
