Understanding the Properties and Limitations of Contrastive Learning for Out-of-Distribution Detection. - Nawid Keshtmand; Raúl Santos-Rodríguez; Jonathan Lawry (ICPR Workshops (1), 2022)

Two-step counterfactual generation for OOD examples. - Nawid Keshtmand; Raúl Santos-Rodríguez; Jonathan Lawry (CoRR, 2023)

TraCE: Trajectory Counterfactual Explanation Scores. - Jeffrey Nicholas Clark; Edward Alexander Small; Nawid Keshtmand; Michelle Wing Lam Wan; Elena Fillola Mayoral; Enrico Werner; Christopher P. Bourdeaux; Raúl Santos-Rodríguez (NLDL, 2024)

Typicality-based point OOD detection with contrastive learning. - Nawid Keshtmand; Raúl Santos-Rodríguez; Jonathan Lawry (NLDL, 2024)

Exploring the Requirements of Clinicians for Explainable AI Decision Support Systems in Intensive Care. - Jeffrey N. Clark; Matthew Wragg; Emily Nielsen; Miquel Perelló-Nieto; Nawid Keshtmand; Michael Ambler; Shiv Sharma; Christopher P. Bourdeaux; Amberly Brigden; Raúl Santos-Rodríguez (CoRR, 2024)

Prototype-enhanced prediction in graph neural networks for climate applications. - Nawid Keshtmand; Elena Fillola; Jeffrey Nicholas Clark; Raúl Santos-Rodríguez; Matthew Rigby (CoRR, 2025)

Weight-Space Linear Recurrent Neural Networks. - Roussel Desmond Nzoyem; Nawid Keshtmand; Idriss Tsayem; David A. W. Barton; Tom Deakin (CoRR, 2025)