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ORCIDScott M. Lundberg
2020 – today
- 2023
[j8]Hugh Chen
, Ian C. Covert, Scott M. Lundberg, Su-In Lee:
Algorithms to estimate Shapley value feature attributions. Nat. Mac. Intell. 5(6): 590-601 (2023)- [c13]Irena Gao, Gabriel Ilharco, Scott M. Lundberg, Marco Túlio Ribeiro:
Adaptive Testing of Computer Vision Models. ICCV 2023: 3980-3991 - [i23]Bhargavi Paranjape, Scott M. Lundberg, Sameer Singh, Hannaneh Hajishirzi, Luke Zettlemoyer, Marco Túlio Ribeiro:
ART: Automatic multi-step reasoning and tool-use for large language models. CoRR abs/2303.09014 (2023) - [i22]Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott M. Lundberg, Harsha Nori, Hamid Palangi, Marco Túlio Ribeiro, Yi Zhang:
Sparks of Artificial General Intelligence: Early experiments with GPT-4. CoRR abs/2303.12712 (2023) - 2022
- [c12]Marco Túlio Ribeiro, Scott M. Lundberg:
Adaptive Testing and Debugging of NLP Models. ACL (1) 2022: 3253-3267 - [c11]Shikhar Murty, Christopher D. Manning, Scott M. Lundberg, Marco Túlio Ribeiro:
Fixing Model Bugs with Natural Language Patches. EMNLP 2022: 11600-11613 - [c10]Mark Hamilton, Scott M. Lundberg, Stephanie Fu, Lei Zhang, William T. Freeman:
Axiomatic Explanations for Visual Search, Retrieval, and Similarity Learning. ICLR 2022 - [i21]Hugh Chen, Ian C. Covert, Scott M. Lundberg, Su-In Lee:
Algorithms to estimate Shapley value feature attributions. CoRR abs/2207.07605 (2022) - [i20]Shikhar Murty, Christopher D. Manning, Scott M. Lundberg, Marco Túlio Ribeiro:
Fixing Model Bugs with Natural Language Patches. CoRR abs/2211.03318 (2022) - [i19]Irena Gao, Gabriel Ilharco, Scott M. Lundberg, Marco Túlio Ribeiro:
Adaptive Testing of Computer Vision Models. CoRR abs/2212.02774 (2022) - 2021
- [j7]Alex Okeson, Rich Caruana, Nick Craswell, Kori Inkpen, Scott M. Lundberg, Harsha Nori, Hanna M. Wallach, Jennifer Wortman Vaughan:
Summarize with Caution: Comparing Global Feature Attributions. IEEE Data Eng. Bull. 44(4): 14-27 (2021) - [j6]Ian Covert, Scott M. Lundberg, Su-In Lee:
Explaining by Removing: A Unified Framework for Model Explanation. J. Mach. Learn. Res. 22: 209:1-209:90 (2021) - [j5]Gabriel G. Erion, Joseph D. Janizek, Pascal Sturmfels, Scott M. Lundberg, Su-In Lee:
Improving performance of deep learning models with axiomatic attribution priors and expected gradients. Nat. Mach. Intell. 3(7): 620-631 (2021) - [j4]Hugh Chen, Scott M. Lundberg, Gabriel G. Erion, Jerry H. Kim, Su-In Lee:
Forecasting adverse surgical events using self-supervised transfer learning for physiological signals. npj Digit. Medicine 4 (2021) - [c9]Jiaxuan Wang, Jenna Wiens, Scott M. Lundberg:
Shapley Flow: A Graph-based Approach to Interpreting Model Predictions. AISTATS 2021: 721-729 - [i18]Mark Hamilton, Scott M. Lundberg, Lei Zhang, Stephanie Fu, William T. Freeman:
Model-Agnostic Explainability for Visual Search. CoRR abs/2103.00370 (2021) - [i17]Hugh Chen, Scott M. Lundberg, Su-In Lee:
Explaining a Series of Models by Propagating Local Feature Attributions. CoRR abs/2105.00108 (2021) - 2020
- [j3]Scott M. Lundberg, Gabriel G. Erion, Hugh Chen, Alex J. DeGrave, Jordan M. Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, Su-In Lee:
From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2(1): 56-67 (2020) - [c8]Rich Caruana, Scott M. Lundberg, Marco Túlio Ribeiro, Harsha Nori, Samuel Jenkins:
Intelligible and Explainable Machine Learning: Best Practices and Practical Challenges. KDD 2020: 3511-3512 - [c7]Ian Covert, Scott M. Lundberg, Su-In Lee:
Understanding Global Feature Contributions With Additive Importance Measures. NeurIPS 2020 - [i16]Hugh Chen, Scott M. Lundberg, Gabriel G. Erion, Jerry H. Kim, Su-In Lee:
Deep Transfer Learning for Physiological Signals. CoRR abs/2002.04770 (2020) - [i15]Ian Covert, Scott M. Lundberg, Su-In Lee:
Understanding Global Feature Contributions Through Additive Importance Measures. CoRR abs/2004.00668 (2020) - [i14]Hugh Chen, Joseph D. Janizek, Scott M. Lundberg, Su-In Lee:
True to the Model or True to the Data? CoRR abs/2006.16234 (2020) - [i13]Jiaxuan Wang, Jenna Wiens, Scott M. Lundberg:
Shapley Flow: A Graph-based Approach to Interpreting Model Predictions. CoRR abs/2010.14592 (2020) - [i12]Ian Covert, Scott M. Lundberg, Su-In Lee:
Feature Removal Is a Unifying Principle for Model Explanation Methods. CoRR abs/2011.03623 (2020) - [i11]Ian Covert, Scott M. Lundberg, Su-In Lee:
Explaining by Removing: A Unified Framework for Model Explanation. CoRR abs/2011.14878 (2020)
2010 – 2019
- 2019
- [b1]Scott M. Lundberg:
Explainable Machine Learning for Science and Medicine. University of Washington, USA, 2019 - [i10]Scott M. Lundberg, Gabriel G. Erion, Hugh Chen, Alex J. DeGrave, Jordan M. Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, Su-In Lee:
Explainable AI for Trees: From Local Explanations to Global Understanding. CoRR abs/1905.04610 (2019) - [i9]Gabriel G. Erion, Joseph D. Janizek, Pascal Sturmfels, Scott M. Lundberg, Su-In Lee:
Learning Explainable Models Using Attribution Priors. CoRR abs/1906.10670 (2019) - [i8]Hugh Chen, Scott M. Lundberg, Su-In Lee:
Explaining Models by Propagating Shapley Values of Local Components. CoRR abs/1911.11888 (2019) - 2018
- [i7]Hugh Chen, Scott M. Lundberg, Su-In Lee:
Hybrid Gradient Boosting Trees and Neural Networks for Forecasting Operating Room Data. CoRR abs/1801.07384 (2018) - [i6]Scott M. Lundberg, Gabriel G. Erion, Su-In Lee:
Consistent Individualized Feature Attribution for Tree Ensembles. CoRR abs/1802.03888 (2018) - 2017
- [c6]Scott M. Lundberg, Su-In Lee:
A Unified Approach to Interpreting Model Predictions. NIPS 2017: 4765-4774 - [i5]Scott M. Lundberg, Su-In Lee:
A unified approach to interpreting model predictions. CoRR abs/1705.07874 (2017) - [i4]Scott M. Lundberg, Su-In Lee:
Consistent feature attribution for tree ensembles. CoRR abs/1706.06060 (2017) - [i3]Hugh Chen, Scott M. Lundberg, Su-In Lee:
Checkpoint Ensembles: Ensemble Methods from a Single Training Process. CoRR abs/1710.03282 (2017) - [i2]Gabriel G. Erion, Hugh Chen, Scott M. Lundberg, Su-In Lee:
Anesthesiologist-level forecasting of hypoxemia with only SpO2 data using deep learning. CoRR abs/1712.00563 (2017) - 2016
- [c5]Naozumi Hiranuma, Scott M. Lundberg, Su-In Lee:
CloudControl: Leveraging many public ChIP-seq control experiments to better remove background noise. BCB 2016: 191-199 - [i1]Scott M. Lundberg, Su-In Lee:
An unexpected unity among methods for interpreting model predictions. CoRR abs/1611.07478 (2016) - 2010
- [j2]Benson L. Joeris, Scott M. Lundberg, Ross M. McConnell:
O(mlogn) split decomposition of strongly-connected graphs. Discret. Appl. Math. 158(7): 779-799 (2010) - [j1]Andrew R. Curtis, Clemente Izurieta, Benson L. Joeris, Scott M. Lundberg, Ross M. McConnell:
An implicit representation of chordal comparability graphs in linear time. Discret. Appl. Math. 158(8): 869-875 (2010) - [c4]Scott M. Lundberg, Randy C. Paffenroth, Jason Yosinski:
Analysis of CBRN sensor fusion methods. FUSION 2010: 1-8
2000 – 2009
- 2009
- [c3]Benson L. Joeris, Scott M. Lundberg, Ross M. McConnell:
O(m logn) Split Decomposition of Strongly Connected Graphs. Graph Theory, Computational Intelligence and Thought 2009: 158-171 - 2008
- [c2]Douglas Moore, John Stevens, Scott M. Lundberg, Bruce A. Draper:
Top down image segmentation using congealing and graph-cut. ICPR 2008: 1-4 - 2006
- [c1]Andrew R. Curtis, Clemente Izurieta, Benson L. Joeris, Scott M. Lundberg, Ross M. McConnell:
An Implicit Representation of Chordal Comparabilty Graphs in Linear-Time. WG 2006: 168-178
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