Research
A full list of my published work can be found on my Google Scholar.
01
NeurIPS 2024
Discovering Preference Optimization Algorithms with and for Large Language ModelsC. Lu, S. Holt, C. Fanconi, A. J. Chan, J. Foerster, M. van der Schaar, & R. T. Lange
02
ICML 2024
Dense Reward for Free in Reinforcement Learning from Human FeedbackA. J. Chan, H. Sun, S. Holt, & M. van der Schaar
03
ICLR 2024
How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated QuestionsL. Pacchiardi, A. J. Chan, S. Mindermann, I. Moscovitz, A. Pan, Y. Gal, O. Evans, & J. M. Brauner
04
NeurIPS Workshop 2023
Optimising Human-AI Collaboration by Finding Convincing ExplanationsA. J. Chan, A. Huyuk, & M. van der Schaar
05
NeurIPS 2022
Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble LearningA. J. Chan & M. van der Schaar
06
NeurIPS Workshop 2022
Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive AttributesT. Liu, A. J. Chan, B. van Breugel, & M. van der Schaar
07
ICLR 2022
Inverse Online Learning: Understanding Non-Stationary and Reactionary PoliciesA. J. Chan, A. Curth & M. van der Schaar
08
ICLR 2022
POETREE: Interpretable Policy Learning with Adaptive Decision TreesA. Pace, A. J. Chan, & M. van der Schaar
09
NeurIPS D&B 2021
The Medkit-learn(ing) Environment: Medical Decision Modelling through SimulationA. J. Chan, I. Bica, A. Huyuk, D. Jarrett, & M. van der Schaar
10
11
ICLR 2021
Generative Time-series Modeling with Fourier FlowsA. M. Alaa, A. J. Chan, & M. van der Schaar
12
ICML 2020
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate ShiftA. J. Chan, A. M. Alaa, Z. Qian, & M. van der Schaar
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