36 citations · 81 across the 8 of their papers we have counts for
8 papers
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 +2
Prompting and fine-tuning have emerged as two competing paradigms for augmenting language models with new capabilities, such as the use of tools. Prompting approaches are quick to…
Task-specific experimental design for treatment effect estimation
Bethany Connolly, Kim Moore, Tobias Schwedes +4
Understanding causality should be a core requirement of any attempt to build real impact through AI. Due to the inherent unobservability of counterfactuals, large randomised trials…
Explainability for fair machine learning
Tom Begley, Tobias Schwedes, Christopher Frye +1
As the decisions made or influenced by machine learning models increasingly impact our lives, it is crucial to detect, understand, and mitigate unfairness. But even simply determin…
Representation Learning for High-Dimensional Data Collection under Local Differential Privacy
Alex Mansbridge, Gregory Barbour, Davide Piras +4
The collection of individuals' data has become commonplace in many industries. Local differential privacy (LDP) offers a rigorous approach to preserving privacy whereby the individ…
Human-interpretable model explainability on high-dimensional data
Damien de Mijolla, Christopher Frye, Markus Kunesch +2
The importance of explainability in machine learning continues to grow, as both neural-network architectures and the data they model become increasingly complex. Unique challenges…
Shapley explainability on the data manifold
Christopher Frye, Damien de Mijolla, Tom Begley +3
Explainability in AI is crucial for model development, compliance with regulation, and providing operational nuance to predictions. The Shapley framework for explainability attribu…
Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability
Christopher Frye, Colin Rowat, Ilya Feige
Explaining AI systems is fundamental both to the development of high performing models and to the trust placed in them by their users. The Shapley framework for explainability has…
Parenting: Safe Reinforcement Learning from Human Input
Christopher Frye, Ilya Feige
Autonomous agents trained via reinforcement learning present numerous safety concerns: reward hacking, negative side effects, and unsafe exploration, among others. In the context o…