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Journal Articles
- 2024
[j4]Jun Yuan
, Brian Barr, Kyle Overton, Enrico Bertini:
Visual Exploration of Machine Learning Model Behavior With Hierarchical Surrogate Rule Sets. IEEE Trans. Vis. Comput. Graph. 30(2): 1470-1488 (2024)- [j3]Parikshit Solunke, Vitória Guardieiro, João Rulff, Peter Xenopoulos, Gromit Yeuk-Yin Chan, Brian Barr, Luis Gustavo Nonato, Cláudio T. Silva:
Mountaineer: Topology-Driven Visual Analytics for Comparing Local Explanations. IEEE Trans. Vis. Comput. Graph. 30(12): 7763-7775 (2024) - 2023
- [j2]Peter Xenopoulos, João Rulff, Luis Gustavo Nonato, Brian Barr, Cláudio T. Silva:
Calibrate: Interactive Analysis of Probabilistic Model Output. IEEE Trans. Vis. Comput. Graph. 29(1): 853-863 (2023) - 2022
- [j1]Jun Yuan, Gromit Yeuk-Yin Chan, Brian Barr, Kyle Overton, Kim Rees, Luis Gustavo Nonato, Enrico Bertini, Cláudio T. Silva:
SUBPLEX: A Visual Analytics Approach to Understand Local Model Explanations at the Subpopulation Level. IEEE Computer Graphics and Applications 42(6): 24-36 (2022)
Conference and Workshop Papers
- 2024
- [c5]Wei Zhang, Brian Barr, John Paisley:
Gaussian Process Neural Additive Models. AAAI 2024: 16865-16872 - 2022
- [c4]Wei Zhang, Brian Barr, John Paisley:
An Interpretable Deep Classifier for Counterfactual Generation. ICAIF 2022: 36-43 - [c3]Wei Zhang, Brian Barr, John Paisley:
Understanding Counterfactual Generation using Maximum Mean Discrepancy. ICAIF 2022: 44-52 - [c2]Peter Xenopoulos, Gromit Chan, Harish Doraiswamy, Luis Gustavo Nonato, Brian Barr, Cláudio T. Silva:
GALE: Globally Assessing Local Explanations. TAG-ML 2022: 322-331 - 2021
- [c1]Rathakrishnan Bhaskaran, Ramakrishnan Kannan, Brian Barr, Stephan Priebe:
Science-Guided Machine Learning for Wall-Modeled Large Eddy Simulation. IEEE BigData 2021: 1809-1816
Informal and Other Publications
- 2024
- [i13]Wei Zhang, Brian Barr, John Paisley:
Gaussian Process Neural Additive Models. CoRR abs/2402.12518 (2024) - [i12]Evandro S. Ortigossa, Fábio F. Dias, Brian Barr, Cláudio T. Silva, Luis Gustavo Nonato:
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients. CoRR abs/2404.16495 (2024) - [i11]Parikshit Solunke, Vitória Guardieiro, João Rulff, Peter Xenopoulos, Gromit Yeuk-Yin Chan, Brian Barr, Luis Gustavo Nonato, Cláudio T. Silva:
MOUNTAINEER: Topology-Driven Visual Analytics for Comparing Local Explanations. CoRR abs/2406.15613 (2024) - 2023
- [i10]Brian Barr, Noah Fatsi, Leif Hancox-Li, Peter Richter, Daniel Proano, Caleb Mok:
The Disagreement Problem in Faithfulness Metrics. CoRR abs/2311.07763 (2023) - 2022
- [i9]Peter Xenopoulos, Gromit Chan, Harish Doraiswamy, Luis Gustavo Nonato, Brian Barr, Cláudio T. Silva:
Topological Representations of Local Explanations. CoRR abs/2201.02155 (2022) - [i8]Jun Yuan, Brian Barr, Kyle Overton, Enrico Bertini:
Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Sets. CoRR abs/2201.07724 (2022) - [i7]Isha Hameed, Samuel Sharpe, Daniel Barcklow, Justin Au-Yeung, Sahil Verma, Jocelyn Huang, Brian Barr, C. Bayan Bruss:
BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial Intelligence. CoRR abs/2207.05566 (2022) - [i6]Peter Xenopoulos, João Rulff, Luis Gustavo Nonato, Brian Barr, Cláudio T. Silva:
Calibrate: Interactive Analysis of Probabilistic Model Output. CoRR abs/2207.13770 (2022) - 2021
- [i5]Brian Barr, Matthew R. Harrington, Samuel Sharpe, C. Bayan Bruss:
Counterfactual Explanations via Latent Space Projection and Interpolation. CoRR abs/2112.00890 (2021) - 2020
- [i4]Brian Barr, Ke Xu, Cláudio T. Silva, Enrico Bertini, Robert Reilly, C. Bayan Bruss, Jason D. Wittenbach:
Towards Ground Truth Explainability on Tabular Data. CoRR abs/2007.10532 (2020) - [i3]Gromit Yeuk-Yin Chan, Jun Yuan, Kyle Overton, Brian Barr, Kim Rees, Luis Gustavo Nonato, Enrico Bertini, Cláudio T. Silva:
SUBPLEX: Towards a Better Understanding of Black Box Model Explanations at the Subpopulation Level. CoRR abs/2007.10609 (2020) - [i2]Gromit Yeuk-Yin Chan, Enrico Bertini, Luis Gustavo Nonato, Brian Barr, Cláudio T. Silva:
Melody: Generating and Visualizing Machine Learning Model Summary to Understand Data and Classifiers Together. CoRR abs/2007.10614 (2020) - [i1]Rachana Balasubramanian, Samuel Sharpe, Brian Barr, Jason D. Wittenbach, C. Bayan Bruss:
Latent-CF: A Simple Baseline for Reverse Counterfactual Explanations. CoRR abs/2012.09301 (2020)
Coauthor Index
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last updated on 2024-11-11 22:23 CET by the dblp team
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