5 citations · 11 across the 7 of their papers we have counts for
9 papers
ShapShift: Explaining Model Prediction Shifts with Subgroup Conditional Shapley Values
Tom Bewley, Salim I. Amoukou, Emanuele Albini +2
Changes in input distribution can induce shifts in the average predictions of machine learning models. Such prediction shifts may impact downstream business outcomes (e.g. a bank's…
Learning Interpretable Models of Aircraft Handling Behaviour by Reinforcement Learning from Human Feedback
Tom Bewley, Jonathan Lawry, Arthur Richards
We propose a method to capture the handling abilities of fast jet pilots in a software model via reinforcement learning (RL) from human preference feedback. We use pairwise prefere…
Reward Learning with Trees: Methods and Evaluation
Tom Bewley, Jonathan Lawry, Arthur Richards +2
Recent efforts to learn reward functions from human feedback have tended to use deep neural networks, whose lack of transparency hampers our ability to explain agent behaviour or v…
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…
Summarising and Comparing Agent Dynamics with Contrastive Spatiotemporal Abstraction
Tom Bewley, Jonathan Lawry, Arthur Richards
We introduce a data-driven, model-agnostic technique for generating a human-interpretable summary of the salient points of contrast within an evolving dynamical system, such as the…
Interpretable Preference-based Reinforcement Learning with Tree-Structured Reward Functions
Tom Bewley, Freddy Lecue
The potential of reinforcement learning (RL) to deliver aligned and performant agents is partially bottlenecked by the reward engineering problem. One alternative to heuristic tria…