4 citations · 9 across the 7 of their papers we have counts for
4 papers · 1 filter
Inherently Interpretable Time Series Classification via Multiple Instance Learning
Joseph Early, Gavin KC Cheung, Kurt Cutajar +3
Conventional Time Series Classification (TSC) methods are often black boxes that obscure inherent interpretation of their decision-making processes. In this work, we leverage Multi…
Inferring Player Location in Sports Matches: Multi-Agent Spatial Imputation from Limited Observations
Gregory Everett, Ryan J. Beal, Tim Matthews +3
Understanding agent behaviour in Multi-Agent Systems (MAS) is an important problem in domains such as autonomous driving, disaster response, and sports analytics. Existing MAS prob…
Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning
Joseph Early, Tom Bewley, Christine Evers +1
We generalise the problem of reward modelling (RM) for reinforcement learning (RL) to handle non-Markovian rewards. Existing work assumes that human evaluators observe each step in…
Model Agnostic Interpretability for Multiple Instance Learning
Joseph Early, Christine Evers, Sarvapali Ramchurn
In Multiple Instance Learning (MIL), models are trained using bags of instances, where only a single label is provided for each bag. A bag label is often only determined by a handf…