Scott M. Lundberg

Marco Túlio Ribeiro

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

Adaptive Testing and Debugging of NLP Models.

[BibT_eX]

[DOI]

Marco Túlio Ribeiro

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022

2021

Forecasting adverse surgical events using self-supervised transfer learning for physiological signals.

[BibT_eX]

[DOI]

npj Digit. Medicine, 2021

Improving performance of deep learning models with axiomatic attribution priors and expected gradients.

[BibT_eX]

[DOI]

Nat. Mach. Intell., 2021

Explaining by Removing: A Unified Framework for Model Explanation.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2021

Summarize with Caution: Comparing Global Feature Attributions.

[BibT_eX]

[DOI]

Jennifer Wortman Vaughan

IEEE Data Eng. Bull., 2021

Explaining a Series of Models by Propagating Local Feature Attributions.

[BibT_eX]

[DOI]

CoRR, 2021

Model-Agnostic Explainability for Visual Search.

[BibT_eX]

[DOI]

CoRR, 2021

Shapley Flow: A Graph-based Approach to Interpreting Model Predictions.

[BibT_eX]

[DOI]

Jiaxuan Wang

Jenna Wiens

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

From local explanations to global understanding with explainable AI for trees.

[BibT_eX]

[DOI]

Nat. Mach. Intell., 2020

Feature Removal Is a Unifying Principle for Model Explanation Methods.

[BibT_eX]

[DOI]

CoRR, 2020

True to the Model or True to the Data?

[BibT_eX]

[DOI]

CoRR, 2020

Understanding Global Feature Contributions Through Additive Importance Measures.

[BibT_eX]

[DOI]

CoRR, 2020

Deep Transfer Learning for Physiological Signals.

[BibT_eX]

[DOI]

CoRR, 2020

Understanding Global Feature Contributions With Additive Importance Measures.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Intelligible and Explainable Machine Learning: Best Practices and Practical Challenges.

[BibT_eX]

[DOI]

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

2019

Explainable Machine Learning for Science and Medicine.

[BibT_eX]

[DOI]

PhD thesis, 2019

Explaining Models by Propagating Shapley Values of Local Components.

[BibT_eX]

[DOI]

CoRR, 2019

Learning Explainable Models Using Attribution Priors.

[BibT_eX]

[DOI]

CoRR, 2019

Explainable AI for Trees: From Local Explanations to Global Understanding.

[BibT_eX]

[DOI]

CoRR, 2019

2018

Consistent Individualized Feature Attribution for Tree Ensembles.

[BibT_eX]

[DOI]

Gabriel G. Erion

CoRR, 2018

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

[BibT_eX]

[DOI]

CoRR, 2018

2017

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

[BibT_eX]

[DOI]

CoRR, 2017

Checkpoint Ensembles: Ensemble Methods from a Single Training Process.

[BibT_eX]

[DOI]

CoRR, 2017

Consistent feature attribution for tree ensembles.

[BibT_eX]

[DOI]

CoRR, 2017

A Unified Approach to Interpreting Model Predictions.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

An unexpected unity among methods for interpreting model predictions.

[BibT_eX]

[DOI]

CoRR, 2016

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

[BibT_eX]

[DOI]

Naozumi Hiranuma

Proceedings of the 7th ACM International Conference on Bioinformatics, 2016

2010

O(mlogn) split decomposition of strongly-connected graphs.

[BibT_eX]

[DOI]

Benson L. Joeris

Ross M. McConnell

Discret. Appl. Math., 2010

An implicit representation of chordal comparability graphs in linear time.

[BibT_eX]

[DOI]

Discret. Appl. Math., 2010

Analysis of CBRN sensor fusion methods.

[BibT_eX]

[DOI]

Randy C. Paffenroth

Jason Yosinski

Proceedings of the 13th Conference on Information Fusion, 2010

2009

O(m logn) Split Decomposition of Strongly Connected Graphs.

[BibT_eX]

[DOI]

Benson L. Joeris