Brian Barr

33 papers A* 1B 2C 1Journal 25Unranked 4
YearRankTypeTitle / Venue / Authors
2026 J jnl
Tabular diffusion counterfactual explanations.
Frontiers Artif. Intell.
Wei Zhang, Brian Barr, John Paisley
2025 J jnl
An Explainable Gaussian Process Auto-encoder for Tabular Data.
CoRR
Wei Zhang, Brian Barr, John Paisley
2025 B conf
Gaussian Process Tilted Nonparametric Density Estimation Using Fisher Divergence Score Matching.
IEEE Big Data
John Paisley, Wei Zhang, Brian Barr
2025 J jnl
Gaussian Process Tilted Nonparametric Density Estimation using Fisher Divergence Score Matching.
CoRR
John Paisley, Wei Zhang, Brian Barr
2025 J jnl
PDPilot: Exploring Partial Dependence Plots Through Ranking, Filtering, and Clustering.
IEEE Trans. Vis. Comput. Graph.
Daniel Kerrigan, Brian Barr, Enrico Bertini
2025 B conf
Stochastic Variational Inference with Tuneable Stochastic Annealing.
IEEE Big Data
John Paisley, Ghazal Fazelnia, Brian Barr
2025 J jnl
Stochastic Variational Inference with Tuneable Stochastic Annealing.
CoRR
John Paisley, Ghazal Fazelnia, Brian Barr
2025 J jnl
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients.
IEEE Intell. Syst.
Evandro S. Ortigossa, Fábio Felix Dias, Brian Barr, Cláudio T. Silva, Luis Gustavo Nonato
2025 J jnl
Tabular Diffusion Counterfactual Explanations.
CoRR
Wei Zhang, Brian Barr, John Paisley
2025 J jnl
Visagreement: Visualizing and Exploring Explanations (Dis)Agreement.
IEEE Trans. Vis. Comput. Graph.
Priscylla Silva, Vitória Guardieiro, Brian Barr, Cláudio T. Silva, Luis Gustavo Nonato
2024 A* conf
Gaussian Process Neural Additive Models.
AAAI
Wei Zhang, Brian Barr, John Paisley
2024 J jnl
Gaussian Process Neural Additive Models.
CoRR
Wei Zhang, Brian Barr, John Paisley
2024 C conf
Gaussian Process Neural Network Embeddings for Collaborative Filtering.
ICMLA
Wei Zhang, Brian Barr, John Paisley
2024 J jnl
MOUNTAINEER: Topology-Driven Visual Analytics for Comparing Local Explanations.
CoRR
Parikshit Solunke, Vitória Guardieiro, João Rulff, Peter Xenopoulos, Gromit Yeuk-Yin Chan, Brian Barr, Luis Gustavo Nonato, Cláudio T. Silva
2024 J jnl
Mountaineer: Topology-Driven Visual Analytics for Comparing Local Explanations.
IEEE Trans. Vis. Comput. Graph.
Parikshit Solunke, Vitória Guardieiro, João Rulff, Peter Xenopoulos, Gromit Yeuk-Yin Chan, Brian Barr, Luis Gustavo Nonato, Cláudio T. Silva
2024 J jnl
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients.
CoRR
Evandro S. Ortigossa, Fábio F. Dias, Brian Barr, Cláudio T. Silva, Luis Gustavo Nonato
2024 J jnl
Visual Exploration of Machine Learning Model Behavior With Hierarchical Surrogate Rule Sets.
IEEE Trans. Vis. Comput. Graph.
Jun Yuan, Brian Barr, Kyle Overton, Enrico Bertini
2023 J jnl
Calibrate: Interactive Analysis of Probabilistic Model Output.
IEEE Trans. Vis. Comput. Graph.
Peter Xenopoulos, João Rulff, Luis Gustavo Nonato, Brian Barr, Cláudio T. Silva
2023 J jnl
The Disagreement Problem in Faithfulness Metrics.
CoRR
Brian Barr, Noah Fatsi, Leif Hancox-Li, Peter Richter, Daniel Proano, Caleb Mok
2022 — conf
An Interpretable Deep Classifier for Counterfactual Generation.
ICAIF
Wei Zhang, Brian Barr, John Paisley
2022 J jnl
BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial Intelligence.
CoRR
Isha Hameed, Samuel Sharpe, Daniel Barcklow, Justin Au-Yeung, Sahil Verma, Jocelyn Huang, Brian Barr, C. Bayan Bruss
2022 J jnl
Calibrate: Interactive Analysis of Probabilistic Model Output.
CoRR
Peter Xenopoulos, João Rulff, Luis Gustavo Nonato, Brian Barr, Cláudio T. Silva
2022 — conf
GALE: Globally Assessing Local Explanations.
TAG-ML
Peter Xenopoulos, Gromit Chan, Harish Doraiswamy, Luis Gustavo Nonato, Brian Barr, Cláudio T. Silva
2022 J jnl
SUBPLEX: A Visual Analytics Approach to Understand Local Model Explanations at the Subpopulation Level.
IEEE Computer Graphics and Applications
Jun Yuan, Gromit Yeuk-Yin Chan, Brian Barr, Kyle Overton, Kim Rees, Luis Gustavo Nonato, Enrico Bertini, Cláudio T. Silva
2022 J jnl
Topological Representations of Local Explanations.
CoRR
Peter Xenopoulos, Gromit Chan, Harish Doraiswamy, Luis Gustavo Nonato, Brian Barr, Cláudio T. Silva
2022 — conf
Understanding Counterfactual Generation using Maximum Mean Discrepancy.
ICAIF
Wei Zhang, Brian Barr, John Paisley
2022 J jnl
Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Sets.
CoRR
Jun Yuan, Brian Barr, Kyle Overton, Enrico Bertini
2021 J jnl
Counterfactual Explanations via Latent Space Projection and Interpolation.
CoRR
Brian Barr, Matthew R. Harrington, Samuel Sharpe, C. Bayan Bruss
2021 — conf
Science-Guided Machine Learning for Wall-Modeled Large Eddy Simulation.
IEEE BigData
Rathakrishnan Bhaskaran, Ramakrishnan Kannan, Brian Barr, Stephan Priebe
2020 J jnl
Latent-CF: A Simple Baseline for Reverse Counterfactual Explanations.
CoRR
Rachana Balasubramanian, Samuel Sharpe, Brian Barr, Jason D. Wittenbach, C. Bayan Bruss
2020 J jnl
Melody: Generating and Visualizing Machine Learning Model Summary to Understand Data and Classifiers Together.
CoRR
Gromit Yeuk-Yin Chan, Enrico Bertini, Luis Gustavo Nonato, Brian Barr, Cláudio T. Silva
2020 J jnl
SUBPLEX: Towards a Better Understanding of Black Box Model Explanations at the Subpopulation Level.
CoRR
Gromit Yeuk-Yin Chan, Jun Yuan, Kyle Overton, Brian Barr, Kim Rees, Luis Gustavo Nonato, Enrico Bertini, Cláudio T. Silva
2020 J jnl
Towards Ground Truth Explainability on Tabular Data.
CoRR
Brian Barr, Ke Xu, Cláudio T. Silva, Enrico Bertini, Robert Reilly, C. Bayan Bruss, Jason D. Wittenbach