Publications | Scott Lundberg

S. Lundberg.

February 2020 Fair & Responsible AI Workshop @ CHI2020

Explaining Quantitative Measures of Fairness

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S. Lundberg, G. Erion, H. Chen, A. DeGrave, J. Prutkin, B. Nair, R. Katz, J. Himmelfarb, N. Bansal, S. Lee.

January 2020 Nature Machine Intelligence, volume 2, pages 56-67

From local explanations to global understanding with explainable AI for trees

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N. Hiranuma, S. Lundberg, S. Lee.

March 2019 to appear in Nucleic Acids Research

AIControl: Replacing matched control experiments with machine learning improves ChIP-seq peak identification

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S. Lundberg, B. Nair, M. Vavilala, M. Horibe, M. Eisses, T. Adams, D. Liston, D. Low, S. Newman, J. Kim, S. Lee.

September 2018 Nature Biomedical Engineering, volume 2, pages 749–760

Explainable machine-learning predictions for the prevention of hypoxaemia during surgery

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S. Lundberg, G. Erion, S. Lee.

May 2018 arXiv

Consistent Individualized Feature Attribution for Tree Ensembles

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S. Lee, S. Celik, B. Logsdon, S. Lundberg, T. Martins, V. Oehler, E. Estey, C. Miller, S. Chien, J. Dai, A. Saxena.

April 2018 Nature communications

A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia

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S. Lundberg, S. Lee.

December 2017 NeurIPS

A unified approach to interpreting model predictions

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G. Erion, H. Chen, S. Lundberg, S. Lee.

December 2017 NeurIPS Workshop ML4H: Machine Learning for Health

Anesthesiologist-level forecasting of hypoxemia with only SpO2 data using deep learning

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H. Chen, S. Lundberg, S. Lee.

December 2017 NeurIPS Workshop ML4H: Machine Learning for Health

Hybrid Gradient Boosting Trees and Neural Networks for Forecasting Operating Room Data

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S. Lundberg, S. Lee.

December 2016 NeurIPS Workshop on Interpretable Machine Learning in Complex Systems

An unexpected unity among methods for interpreting model predictions

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N. Hiranuma, S. Lundberg, S. Lee.

December 2016 in Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM

CloudControl: Leveraging many public ChIP-seq control experiments to better remove background noise

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S. Lundberg, W. Tu, B. Raught, L. Penn, M. Hoffman, S. Lee.

June 2016 Genome Biology

ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data

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R. Nong, R. Paffenroth, S. Lundberg, W. Leed.

June 2014 U.S. Patent # US8811758

Method for Lossy Compression of Point Clouds with Pointwise Error Constraints

S. Lundberg, C. Calderon, R. Paffenroth.

June 2012 in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 8393, p. 1

Detecting Clustered Chem/Bio Signals in Noisy Sensor Feeds Using Adaptive Fusion

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C. Calderon, A. Jones, S. Lundberg, R. Paffenroth.

June 2011 in Proceedings of SPIE, vol. 8137, p. 813704

A data-driven approach for processing heterogeneous categorical sensor signals

B. Joeris, S. Lundberg, R. McConnell.

June 2010 Discrete Applied Mathematics, vol. 158, no. 7, pp. 779–799

O (mlogn) split decomposition of strongly-connected graphs

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S. Lundberg, R. Paffenroth, J. Yosinski.

June 2010 Information Fusion (FUSION), 2010 13th Conference on, pp. 1–8, IEEE

Analysis of CBRN sensor fusion methods

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A. Curtis, C. Izurieta, B. Joeris, S. Lundberg, R. McConnell.

June 2010 Discrete Applied Mathematics, vol. 158, no. 8, pp. 869–875

An implicit representation of chordal comparability graphs in linear time

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S. Lundberg, R. Paffenroth, J. Yosinski.

June 2010 Proceedings of SPIE, vol. 7698, p. 769806

Algorithms for Distributed Chemical Sensor Fusion

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S. Lundberg.

June 2008 Master’s thesis, Colorado State University

O (m log n) split decomposition of directed graphs

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D. Moore, J. Stevens, S. Lundberg, B. Draper.

March 2008 in Pattern Recognition, 2008. ICPR 2008. 19th International Conference on, pp. 1–4, IEEE

Top down image segmentation using congealing and graph-cut

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