15 citations · 37 across the 11 of their papers we have counts for
11 papers
Discovering Preference Optimization Algorithms with and for Large Language Models
Chris Lu, Samuel Holt, Claudio Fanconi +4
Offline preference optimization is a key method for enhancing and controlling the quality of Large Language Model (LLM) outputs. Typically, preference optimization is approached as…
Dense Reward for Free in Reinforcement Learning from Human Feedback
Alex J. Chan, Hao Sun, Samuel Holt +1
Reinforcement Learning from Human Feedback (RLHF) has been credited as the key advance that has allowed Large Language Models (LLMs) to effectively follow instructions and produce…
Optimising Human-AI Collaboration by Learning Convincing Explanations
Alex J. Chan, Alihan Huyuk, Mihaela van der Schaar
Machine learning models are being increasingly deployed to take, or assist in taking, complicated and high-impact decisions, from quasi-autonomous vehicles to clinical decision sup…
When is Off-Policy Evaluation (Reward Modeling) Useful in Contextual Bandits? A Data-Centric Perspective
Hao Sun, Alex J. Chan, Nabeel Seedat +2
Evaluating the value of a hypothetical target policy with only a logged dataset is important but challenging. On the one hand, it brings opportunities for safe policy improvement u…
Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributes
Tennison Liu, Alex J. Chan, Boris van Breugel +1
It is important to guarantee that machine learning algorithms deployed in the real world do not result in unfairness or unintended social consequences. Fair ML has largely focused…
Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning
Alex J. Chan, Mihaela van der Schaar
Consider making a prediction over new test data without any opportunity to learn from a training set of labelled data - instead given access to a set of expert models and their pre…
POETREE: Interpretable Policy Learning with Adaptive Decision Trees
Alizée Pace, Alex J. Chan, Mihaela van der Schaar
Building models of human decision-making from observed behaviour is critical to better understand, diagnose and support real-world policies such as clinical care. As established po…
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies
Alex J. Chan, Alicia Curth, Mihaela van der Schaar
Human decision making is well known to be imperfect and the ability to analyse such processes individually is crucial when attempting to aid or improve a decision-maker's ability t…
The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation
Alex J. Chan, Ioana Bica, Alihan Huyuk +2
Understanding decision-making in clinical environments is of paramount importance if we are to bring the strengths of machine learning to ultimately improve patient outcomes. Sever…
Scalable Bayesian Inverse Reinforcement Learning
Alex J. Chan, Mihaela van der Schaar
Bayesian inference over the reward presents an ideal solution to the ill-posed nature of the inverse reinforcement learning problem. Unfortunately current methods generally do not…
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift
Alex J. Chan, Ahmed M. Alaa, Zhaozhi Qian +1
Modern neural networks have proven to be powerful function approximators, providing state-of-the-art performance in a multitude of applications. They however fall short in their ab…