Parenting: Safe Reinforcement Learning from Human Input. - Christopher Frye; Ilya Feige (CoRR, 2019)

Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability. - Christopher Frye; Colin Rowat; Ilya Feige (NeurIPS, 2020)

Shapley-based explainability on the data manifold. - Christopher Frye; Damien de Mijolla; Laurence Cowton; Megan Stanley; Ilya Feige (CoRR, 2020)

Human-interpretable model explainability on high-dimensional data. - Damien de Mijolla; Christopher Frye; Markus Kunesch; John Mansir; Ilya Feige (CoRR, 2020)

Explainability for fair machine learning. - Tom Begley; Tobias Schwedes; Christopher Frye; Ilya Feige (CoRR, 2020)

Learning to Noise: Application-Agnostic Data Sharing with Local Differential Privacy. - Alex Mansbridge; Gregory Barbour; Davide Piras; Christopher Frye; Ilya Feige; David Barber (CoRR, 2020)

Shapley explainability on the data manifold. - Christopher Frye; Damien de Mijolla; Tom Begley; Laurence Cowton; Megan Stanley; Ilya Feige (ICLR, 2021)

Task-specific experimental design for treatment effect estimation. - Bethany Connolly; Kim Moore; Tobias Schwedes; Alexander Adam; Gary Willis; Ilya Feige; Christopher Frye (ICML, 2023)

Language hooks: a modular framework for augmenting LLM reasoning that decouples tool usage from the model and its prompt. - Damien de Mijolla; Wen Yang; Philippa Duckett; Christopher Frye; Mark Worrall (CoRR, 2024)