Scott M. Lundberg

45 papers A* 5A 1B 2C 1Journal 32Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Sequential Diagnosis with Language Models. CoRR Harsha Nori, Mayank Daswani, Christopher Kelly, Scott M. Lundberg, Marco Túlio Ribeiro, Marc Wilson, Xiaoxuan Liu, Viknesh Sounderajah, Jonathan Carlson, Matthew P. Lungren, Bay Gross, Peter Hames, Mustafa Suleyman, Dominic King, Eric Horvitz
2023	J	`jnl`	ART: Automatic multi-step reasoning and tool-use for large language models. CoRR Bhargavi Paranjape, Scott M. Lundberg, Sameer Singh, Hannaneh Hajishirzi, Luke Zettlemoyer, Marco Túlio Ribeiro
2023	A*	`conf`	Adaptive Testing of Computer Vision Models. ICCV Irena Gao, Gabriel Ilharco, Scott M. Lundberg, Marco Túlio Ribeiro
2023	J	`jnl`	Algorithms to estimate Shapley value feature attributions. Nat. Mac. Intell. Hugh Chen, Ian C. Covert, Scott M. Lundberg, Su-In Lee
2023	J	`jnl`	Sparks of Artificial General Intelligence: Early experiments with GPT-4. CoRR 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
2022	—	`conf`	Adaptive Testing and Debugging of NLP Models. ACL (1) Marco Túlio Ribeiro, Scott M. Lundberg
2022	J	`jnl`	Adaptive Testing of Computer Vision Models. CoRR Irena Gao, Gabriel Ilharco, Scott M. Lundberg, Marco Túlio Ribeiro
2022	J	`jnl`	Algorithms to estimate Shapley value feature attributions. CoRR Hugh Chen, Ian C. Covert, Scott M. Lundberg, Su-In Lee
2022	A*	`conf`	Axiomatic Explanations for Visual Search, Retrieval, and Similarity Learning. ICLR Mark Hamilton, Scott M. Lundberg, Stephanie Fu, Lei Zhang, William T. Freeman
2022	A*	`conf`	Fixing Model Bugs with Natural Language Patches. EMNLP Shikhar Murty, Christopher D. Manning, Scott M. Lundberg, Marco Túlio Ribeiro
2022	J	`jnl`	Fixing Model Bugs with Natural Language Patches. CoRR Shikhar Murty, Christopher D. Manning, Scott M. Lundberg, Marco Túlio Ribeiro
2021	J	`jnl`	Explaining a Series of Models by Propagating Local Feature Attributions. CoRR Hugh Chen, Scott M. Lundberg, Su-In Lee
2021	J	`jnl`	Explaining by Removing: A Unified Framework for Model Explanation. J. Mach. Learn. Res. Ian Covert, Scott M. Lundberg, Su-In Lee
2021	J	`jnl`	Forecasting adverse surgical events using self-supervised transfer learning for physiological signals. npj Digit. Medicine Hugh Chen, Scott M. Lundberg, Gabriel G. Erion, Jerry H. Kim, Su-In Lee
2021	J	`jnl`	Improving performance of deep learning models with axiomatic attribution priors and expected gradients. Nat. Mach. Intell. Gabriel G. Erion, Joseph D. Janizek, Pascal Sturmfels, Scott M. Lundberg, Su-In Lee
2021	J	`jnl`	Model-Agnostic Explainability for Visual Search. CoRR Mark Hamilton, Scott M. Lundberg, Lei Zhang, Stephanie Fu, William T. Freeman
2021	A	`conf`	Shapley Flow: A Graph-based Approach to Interpreting Model Predictions. AISTATS Jiaxuan Wang, Jenna Wiens, Scott M. Lundberg
2021	J	`jnl`	Summarize with Caution: Comparing Global Feature Attributions. IEEE Data Eng. Bull. Alex Okeson, Rich Caruana, Nick Craswell, Kori Inkpen, Scott M. Lundberg, Harsha Nori, Hanna M. Wallach, Jennifer Wortman Vaughan
2020	J	`jnl`	Deep Transfer Learning for Physiological Signals. CoRR Hugh Chen, Scott M. Lundberg, Gabriel G. Erion, Jerry H. Kim, Su-In Lee
2020	J	`jnl`	Explaining by Removing: A Unified Framework for Model Explanation. CoRR Ian Covert, Scott M. Lundberg, Su-In Lee
2020	J	`jnl`	Feature Removal Is a Unifying Principle for Model Explanation Methods. CoRR Ian Covert, Scott M. Lundberg, Su-In Lee
2020	J	`jnl`	From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 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
2020	A*	`conf`	Intelligible and Explainable Machine Learning: Best Practices and Practical Challenges. KDD Rich Caruana, Scott M. Lundberg, Marco Túlio Ribeiro, Harsha Nori, Samuel Jenkins
2020	J	`jnl`	Shapley Flow: A Graph-based Approach to Interpreting Model Predictions. CoRR Jiaxuan Wang, Jenna Wiens, Scott M. Lundberg
2020	J	`jnl`	True to the Model or True to the Data? CoRR Hugh Chen, Joseph D. Janizek, Scott M. Lundberg, Su-In Lee
2020	J	`jnl`	Understanding Global Feature Contributions Through Additive Importance Measures. CoRR Ian Covert, Scott M. Lundberg, Su-In Lee
2020	A*	`conf`	Understanding Global Feature Contributions With Additive Importance Measures. NeurIPS Ian Covert, Scott M. Lundberg, Su-In Lee
2019	J	`jnl`	Explainable AI for Trees: From Local Explanations to Global Understanding. CoRR 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
2019	—	`—`	Explainable Machine Learning for Science and Medicine. Scott M. Lundberg
2019	J	`jnl`	Explaining Models by Propagating Shapley Values of Local Components. CoRR Hugh Chen, Scott M. Lundberg, Su-In Lee
2019	J	`jnl`	Learning Explainable Models Using Attribution Priors. CoRR Gabriel G. Erion, Joseph D. Janizek, Pascal Sturmfels, Scott M. Lundberg, Su-In Lee
2018	J	`jnl`	Consistent Individualized Feature Attribution for Tree Ensembles. CoRR Scott M. Lundberg, Gabriel G. Erion, Su-In Lee
2018	J	`jnl`	Hybrid Gradient Boosting Trees and Neural Networks for Forecasting Operating Room Data. CoRR Hugh Chen, Scott M. Lundberg, Su-In Lee
2017	—	`conf`	A Unified Approach to Interpreting Model Predictions. NIPS Scott M. Lundberg, Su-In Lee
2017	J	`jnl`	A unified approach to interpreting model predictions. CoRR Scott M. Lundberg, Su-In Lee
2017	J	`jnl`	Anesthesiologist-level forecasting of hypoxemia with only SpO2 data using deep learning. CoRR Gabriel G. Erion, Hugh Chen, Scott M. Lundberg, Su-In Lee
2017	J	`jnl`	Checkpoint Ensembles: Ensemble Methods from a Single Training Process. CoRR Hugh Chen, Scott M. Lundberg, Su-In Lee
2017	J	`jnl`	Consistent feature attribution for tree ensembles. CoRR Scott M. Lundberg, Su-In Lee
2016	J	`jnl`	An unexpected unity among methods for interpreting model predictions. CoRR Scott M. Lundberg, Su-In Lee
2016	—	`conf`	CloudControl: Leveraging many public ChIP-seq control experiments to better remove background noise. BCB Naozumi Hiranuma, Scott M. Lundberg, Su-In Lee
2010	J	`jnl`	An implicit representation of chordal comparability graphs in linear time. Discret. Appl. Math. Andrew R. Curtis, Clemente Izurieta, Benson L. Joeris, Scott M. Lundberg, Ross M. McConnell
2010	C	`conf`	Analysis of CBRN sensor fusion methods. FUSION Scott M. Lundberg, Randy C. Paffenroth, Jason Yosinski
2010	J	`jnl`	O(mlogn) split decomposition of strongly-connected graphs. Discret. Appl. Math. Benson L. Joeris, Scott M. Lundberg, Ross M. McConnell
2008	B	`conf`	Top down image segmentation using congealing and graph-cut. ICPR Douglas Moore, John Stevens, Scott M. Lundberg, Bruce A. Draper
2006	B	`conf`	An Implicit Representation of Chordal Comparabilty Graphs in Linear-Time. WG Andrew R. Curtis, Clemente Izurieta, Benson L. Joeris, Scott M. Lundberg, Ross M. McConnell