148 citations · 176 across the 10 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
cs.LG2022★ 4 cited
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…
cs.LG2022★ 1 cited
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…