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)

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)

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)

Latent-CF: A Simple Baseline for Reverse Counterfactual Explanations. - Rachana Balasubramanian; Samuel Sharpe; Brian Barr; Jason D. Wittenbach; C. Bayan Bruss (CoRR, 2020)

Science-Guided Machine Learning for Wall-Modeled Large Eddy Simulation. - Rathakrishnan Bhaskaran; Ramakrishnan Kannan; Brian Barr; Stephan Priebe (IEEE BigData, 2021)

Counterfactual Explanations via Latent Space Projection and Interpolation. - Brian Barr; Matthew R. Harrington; Samuel Sharpe; C. Bayan Bruss (CoRR, 2021)

An Interpretable Deep Classifier for Counterfactual Generation. - Wei Zhang 262; Brian Barr; John Paisley (ICAIF, 2022)

Understanding Counterfactual Generation using Maximum Mean Discrepancy. - Wei Zhang 262; Brian Barr; John Paisley (ICAIF, 2022)

GALE: Globally Assessing Local Explanations. - Peter Xenopoulos; Gromit Chan; Harish Doraiswamy; Luis Gustavo Nonato; Brian Barr; Cláudio T. Silva (TAG-ML, 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)

Topological Representations of Local Explanations. - Peter Xenopoulos; Gromit Chan; Harish Doraiswamy; Luis Gustavo Nonato; Brian Barr; Cláudio T. Silva (CoRR, 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)

The Disagreement Problem in Faithfulness Metrics. - Brian Barr; Noah Fatsi; Leif Hancox-Li; Peter Richter; Daniel Proano; Caleb Mok (CoRR, 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)

Gaussian Process Neural Additive Models. - Wei Zhang 262; Brian Barr; John Paisley (AAAI, 2024)

Gaussian Process Neural Network Embeddings for Collaborative Filtering. - Wei Zhang 262; Brian Barr; John Paisley (ICMLA, 2024)

T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients. - Evandro S. Ortigossa; Fábio F. Dias 001; Brian Barr; Cláudio T. Silva; Luis Gustavo Nonato (CoRR, 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., 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., 2024)

Gaussian Process Tilted Nonparametric Density Estimation using Fisher Divergence Score Matching. - John Paisley; Wei Zhang 262; Brian Barr (CoRR, 2025)

Stochastic Variational Inference with Tuneable Stochastic Annealing. - John Paisley; Ghazal Fazelnia; Brian Barr (CoRR, 2025)