2 citations · 6 across the 11 of their papers we have counts for
5 papers · 1 filter
Temporal-Difference Variational Continual Learning
Luckeciano C. Melo, Alessandro Abate, Yarin Gal
Machine Learning models in real-world applications must continuously learn new tasks to adapt to shifts in the data-generating distribution. Yet, for Continual Learning (CL), model…
TextCAVs: Debugging vision models using text
Angus Nicolson, Yarin Gal, J. Alison Noble
Concept-based interpretability methods are a popular form of explanation for deep learning models which provide explanations in the form of high-level human interpretable concepts.…
Challenges and Considerations in the Evaluation of Bayesian Causal Discovery
Amir Mohammad Karimi Mamaghan, Panagiotis Tigas, Karl Henrik Johansson +3
Representing uncertainty in causal discovery is a crucial component for experimental design, and more broadly, for safe and reliable causal decision making. Bayesian Causal Discove…
Deep Bayesian Active Learning for Preference Modeling in Large Language Models
Luckeciano C. Melo, Panagiotis Tigas, Alessandro Abate +1
Leveraging human preferences for steering the behavior of Large Language Models (LLMs) has demonstrated notable success in recent years. Nonetheless, data selection and labeling ar…
Explaining Explainability: Recommendations for Effective Use of Concept Activation Vectors
Angus Nicolson, Lisa Schut, J. Alison Noble +1
Concept-based explanations translate the internal representations of deep learning models into a language that humans are familiar with: concepts. One popular method for finding co…