Brian Barr

According to our database, Brian Barr authored at least 18 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Mountaineer: Topology-Driven Visual Analytics for Comparing Local Explanations.

Parikshit Solunke, Vitória Guardieiro, João Rulff, Peter Xenopoulos, Gromit Yeuk-Yin Chan, Brian Barr, Luis Gustavo Nonato, Cláudio T. Silva

IEEE Trans. Vis. Comput. Graph., December, 2024

Visual Exploration of Machine Learning Model Behavior With Hierarchical Surrogate Rule Sets.

Jun Yuan, Brian Barr, Kyle Overton, Enrico Bertini

IEEE Trans. Vis. Comput. Graph., February, 2024

T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients.

Evandro S. Ortigossa, Fábio F. Dias, Brian Barr, Cláudio T. Silva, Luis Gustavo Nonato

CoRR, 2024

Gaussian Process Neural Additive Models.

Wei Zhang, Brian Barr, John Paisley

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Calibrate: Interactive Analysis of Probabilistic Model Output.

Peter Xenopoulos, João Rulff, Luis Gustavo Nonato, Brian Barr, Cláudio T. Silva

IEEE Trans. Vis. Comput. Graph., 2023

The Disagreement Problem in Faithfulness Metrics.

Brian Barr, Noah Fatsi, Leif Hancox-Li, Peter Richter, Daniel Proano, Caleb Mok

CoRR, 2023

2022

BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial Intelligence.

Isha Hameed, Samuel Sharpe, Daniel Barcklow, Justin Au-Yeung, Sahil Verma, Jocelyn Huang, Brian Barr, C. Bayan Bruss

CoRR, 2022

Topological Representations of Local Explanations.

Peter Xenopoulos, Gromit Chan, Harish Doraiswamy, Luis Gustavo Nonato, Brian Barr, Cláudio T. Silva

CoRR, 2022

SUBPLEX: A Visual Analytics Approach to Understand Local Model Explanations at the Subpopulation Level.

Jun Yuan, Gromit Yeuk-Yin Chan, Brian Barr, Kyle Overton, Kim Rees, Luis Gustavo Nonato, Enrico Bertini, Cláudio T. Silva

IEEE Computer Graphics and Applications, 2022

GALE: Globally Assessing Local Explanations.

Peter Xenopoulos, Gromit Chan, Harish Doraiswamy, Luis Gustavo Nonato, Brian Barr, Cláudio T. Silva

Proceedings of the Topological, 2022

Understanding Counterfactual Generation using Maximum Mean Discrepancy.

Wei Zhang, Brian Barr, John Paisley

Proceedings of the 3rd ACM International Conference on AI in Finance, 2022

An Interpretable Deep Classifier for Counterfactual Generation.

Wei Zhang, Brian Barr, John Paisley

Proceedings of the 3rd ACM International Conference on AI in Finance, 2022

2021

Counterfactual Explanations via Latent Space Projection and Interpolation.

Brian Barr, Matthew R. Harrington, Samuel Sharpe, C. Bayan Bruss

CoRR, 2021

Science-Guided Machine Learning for Wall-Modeled Large Eddy Simulation.

Rathakrishnan Bhaskaran, Ramakrishnan Kannan, Brian Barr, Stephan Priebe

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

2020

Latent-CF: A Simple Baseline for Reverse Counterfactual Explanations.

Rachana Balasubramanian, Samuel Sharpe, Brian Barr, Jason D. Wittenbach, C. Bayan Bruss

CoRR, 2020

Melody: Generating and Visualizing Machine Learning Model Summary to Understand Data and Classifiers Together.

Gromit Yeuk-Yin Chan, Enrico Bertini, Luis Gustavo Nonato, Brian Barr, Cláudio T. Silva

CoRR, 2020

SUBPLEX: Towards a Better Understanding of Black Box Model Explanations at the Subpopulation Level.

Gromit Yeuk-Yin Chan, Jun Yuan, Kyle Overton, Brian Barr, Kim Rees, Luis Gustavo Nonato, Enrico Bertini, Cláudio T. Silva

CoRR, 2020

Towards Ground Truth Explainability on Tabular Data.

Brian Barr, Ke Xu, Cláudio T. Silva, Enrico Bertini, Robert Reilly, C. Bayan Bruss, Jason D. Wittenbach

CoRR, 2020