Proper Losses for Learning with Example-Dependent Costs. - Alexander Hepburn; Ryan McConville; Raúl Santos-Rodríguez; Jesús Cid-Sueiro; Dario García-García (LIDTA@ECML/PKDD, 2018)

bLIMEy: Surrogate Prediction Explanations Beyond LIME. - Kacper Sokol; Alexander Hepburn; Raúl Santos-Rodríguez; Peter A. Flach (CoRR, 2019)

Perceptnet: A Human Visual System Inspired Neural Network For Estimating Perceptual Distance. - Alexander Hepburn; Valero Laparra; Jesús Malo; Ryan McConville; Raúl Santos-Rodríguez (ICIP, 2020)

Enforcing perceptual consistency on Generative Adversarial Networks by using the Normalised Laplacian Pyramid Distance. - Alexander Hepburn; Valero Laparra; Ryan McConville; Raúl Santos-Rodríguez (NLDL, 2020)

FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems. - Kacper Sokol; Alexander Hepburn; Rafael Poyiadzi; Matthew Clifford; Raúl Santos-Rodríguez; Peter A. Flach (J. Open Source Softw., 2020)

FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems. - Kacper Sokol; Alexander Hepburn; Rafael Poyiadzi; Matthew Clifford; Raúl Santos-Rodríguez; Peter A. Flach (Zenodo, 2020)

Explainers in the Wild: Making Surrogate Explainers Robust to Distortions Through Perception. - Alexander Hepburn; Raúl Santos-Rodríguez (ICIP, 2021)

Sampling Based On Natural Image Statistics Improves Local Surrogate Explainers. - Ricardo Kleinlein; Alexander Hepburn; Raúl Santos-Rodríguez; Fernando Fernández Martínez (BMVC, 2022)

Orthonormal Convolutions for the Rotation Based Iterative Gaussianization. - Valero Laparra; Alexander Hepburn; Juan Emmanuel Johnson; Jesús Malo (ICIP, 2022)

On the relation between statistical learning and perceptual distances. - Alexander Hepburn; Valero Laparra; Raúl Santos-Rodríguez; Johannes Ballé; Jesus Malo (ICLR, 2022)

What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components. - Kacper Sokol; Alexander Hepburn; Raúl Santos-Rodríguez; Peter A. Flach (CoRR, 2022)

What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components. - Kacper Sokol; Alexander Hepburn; Raúl Santos-Rodriguez; Peter A. Flach (Zenodo, 2022)

Reconciling Training and Evaluation Objectives in Location Agnostic Surrogate Explainers. - Matthew Clifford; Jonathan Erskine; Alexander Hepburn; Peter A. Flach; Raúl Santos-Rodríguez (CIKM, 2023)

Identification, explanation and clinical evaluation of hospital patient subtypes. - Enrico Werner; Jeffrey N. Clark; Ranjeet S. Bhamber; Michael Ambler; Christopher P. Bourdeaux; Alexander Hepburn; Christopher J. McWilliams; Raúl Santos-Rodríguez (CoRR, 2023)

Disentangling the Link Between Image Statistics and Human Perception. - Alexander Hepburn; Valero Laparra; Raúl Santos-Rodríguez; Jesús Malo (CoRR, 2023)

What You Hear Is What You See: Audio Quality Metrics From Image Quality Metrics. - Tashi Namgyal; Alexander Hepburn; Raúl Santos-Rodríguez; Valero Laparra; Jesus Malo (CoRR, 2023)

Data is Overrated: Perceptual Metrics Can Lead Learning in the Absence of Training Data. - Tashi Namgyal; Alexander Hepburn; Raúl Santos-Rodríguez; Valero Laparra; Jesus Malo (CoRR, 2023)

Learning Confidence Bounds for Classification with Imbalanced Data. - Matthew Clifford; Jonathan Erskine; Alexander Hepburn; Raúl Santos-Rodríguez; Dario García-García (ECAI, 2024)

An Interactive Human-Machine Learning Interface for Collecting and Learning from Complex Annotations. - Jonathan Erskine; Matthew Clifford; Alexander Hepburn; Raúl Santos-Rodríguez (IJCAI, 2024)

Evaluating Perceptual Distances by Fitting Binomial Distributions to Two-Alternative Forced Choice Data. - Alexander Hepburn; Raúl Santos-Rodriguez; Javier Portilla (CoRR, 2024)

The Effect of Perceptual Metrics on Music Representation Learning for Genre Classification. - Tashi Namgyal; Alexander Hepburn; Raúl Santos-Rodríguez; Valero Laparra; Jesus Malo (CoRR, 2024)

IQM-Vis: A user-centric python toolbox for visualising and evaluating image quality metrics. - Matthew Clifford; Alexander Hepburn; Ricardo Kleinlein; Jorge Vila-Tomás; Pablo Hernández-Cámara; Paula Dauden; Nathan F. Lepora; Valero Laparra; Raúl Santos-Rodríguez (SoftwareX, 2025)