You can also find my articles on my Google Scholar profile.
Published in 3rd International Conference on Explainable AI, 2025
This paper presents a suite of interactive mechanisms that enables human-in-the-loop inference of reinforcement learning constraints.
Recommended citation: Deane, O., Ray, O. (2025). A Human-in-the-Loop Approach to Learning Social Norms and Behavioural Policies.. 3rd International Conference on Explainable AI. 1(1).
Published in 19th Conference on Neurosymbolic Learning and Reasoning, 2025
This paper presents a neuro-symbolic framework that derives social norms governing an environment by interleaving exploration-driven neural policy learning with symbolic norm generation from expert examples.
Recommended citation: Deane, O., & Ray, O. Neuro-Symbolic Inverse Constrained Reinforcement Learning. In 19th International Conference on Neurosymbolic Learning and Reasoning.
Published in NeurIPS 2022: Workshop on Human in the Loop Learning, 2022
This paper explores how logic-based Human-in-the-loop Learning (HILL) systems can help users more effectively deal with confounding factors in relational data.
Recommended citation: Deane, O., Ray, O. (2022). End-user-centered Interactive Explanatory Relational Learning with Inductive Logic Programming. Human in the Loop Learning at NeurIPS 2022. 1(1).
Published in Behavior Research Methods, 2022
This paper presents a novel system for automatic annotation of gaze tracking data using Mask R-CNN.
Recommended citation: Deane, O., Toth, E., Yeo, S. (2022). Deep SAGA: a Deep-learning-based System for Automatic Gaze Annotation from Eye-tracking Data. Behavior Research Methods. 1(1). https://link.springer.com/article/10.3758/s13428-022-01833-4