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Thinking about things. |
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I am interested in developing safe and robust machine learning systems that behave in the way we expect, and thus able to work with humans effectively. My work has often explored inverse reinforcement learning and imitation learning in order to learn from humans, and I’m interested in using this knowledge and generative AI to create insight and develop personalised, human-centric, decision making systems.
A full list of my published work can be found on my [Google Scholar](https://scholar.google.com/citations?user=yfy_BGIAAAAJ&hl=en&oi=sra){:target="_blank"}
L. Pacchiardi, A. J. Chan, S. Mindermann, I. Moscovitz, A. Pan, Y. Gal, O. Evans, & J. M. Brauner How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions{:target="_blank"}, International Conference on Learning Representations (ICLR), 2024. PDF{:target="_blank"}
A. J. Chan, A. Huyuk, & M. van der Schaar Optimising Human-AI Collaboration by Finding Convincing Explanations{:target="_blank"}, NeurIPS XAI in Action, 2023. PDF{:target="_blank"}
A. J. Chan & M. van der Schaar Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning{:target="_blank"}, Advances in Neural Information Processing Systems (NeurIPS), 2022. PDF{:target="_blank"}
T. Liu, A. J. Chan, B. van Breugel, & M. van der Schaar Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributes{:target="_blank"}, Algorithmic Fairness through the Lens of Causality and Privacy (AFCP) at NeurIPS, 2022. PDF{:target="_blank"}
A. J. Chan, A. Curth & M. van der Schaar Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies{:target="_blank"}, International Conference on Learning Representations (ICLR), 2022. PDF{:target="_blank"}
A. Pace, A. J. Chan, & M. van der Schaar POETREE: Interpretable Policy Learning with Adaptive Decision Trees{:target="_blank"}, International Conference on Learning Representations (ICLR), 2022. PDF{:target="_blank"}
A. J. Chan, I. Bica, A. Huyuk, D. Jarrett, & M. van der Schaar The Medkit-learn(ing) Environment: Medical Decision Modelling through Simulation{:target="_blank"}, Proceedings of the Neural Information Processing Systems (NeurIPS) track on Datasets and Benchmarks, 2021. PDF{:target="_blank"}
A. J. Chan & M. van der Schaar Scalable Bayesian Inverse Reinforcement Learning{:target="_blank"}, International Conference on Learning Representations (ICLR), 2021. PDF{:target="_blank"}
A. M. Alaa, A. J. Chan, & M. van der Schaar Generative Time-series Modeling with Fourier Flows{:target="_blank"}, International Conference on Learning Representations (ICLR), 2021. PDF{:target="_blank"}
A. J. Chan, A. M. Alaa, Z. Qian, & M. van der Schaar Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift{:target="_blank"}, International Conference on Machine Learning (ICML), 2020. PDF{:target="_blank"}
A. J. Chan & M. van der Schaar Interpretable Policy Learning{:target="_blank"}, MPhil Machine Learning and Machine Intelligence Thesis, 2020.
A. J. Chan & R. Silva Probabilistic Deep Learning{:target="_blank"}, BSc Statistics Thesis, 2019.