Alex J. Chan - Research

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Highlighted Publications

A full list of my published work can be found on my Google Scholar.

  1. C. Lu, S. Holt, C. Fanconi A. J. Chan, J. Foerster, M. van der Schaar, & R. T. Lange Discovering Preference Optimization Algorithms with and for Large Language Models, Advances in Neural Information Processing Systems (NeurIPS), 2024. PDF
  2. A. J. Chan, H. Sun, S. Holt, & M. van der Schaar Dense Reward for Free in Reinforcement Learning from Human Feedback, International Conference on Machine Learning (ICML), 2024. PDF
  3. 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, International Conference on Learning Representations (ICLR), 2024. PDF
  4. A. J. Chan, A. Huyuk, & M. van der Schaar Optimising Human-AI Collaboration by Finding Convincing Explanations, NeurIPS XAI in Action, 2023. PDF
  5. A. J. Chan & M. van der Schaar Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning, Advances in Neural Information Processing Systems (NeurIPS), 2022. PDF
  6. T. Liu, A. J. Chan, B. van Breugel, & M. van der Schaar Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributes, Algorithmic Fairness through the Lens of Causality and Privacy (AFCP) at NeurIPS, 2022. PDF
  7. A. J. Chan, A. Curth & M. van der Schaar Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies, International Conference on Learning Representations (ICLR), 2022. PDF
  8. A. Pace, A. J. Chan, & M. van der Schaar POETREE: Interpretable Policy Learning with Adaptive Decision Trees, International Conference on Learning Representations (ICLR), 2022. PDF
  9. A. J. Chan, I. Bica, A. Huyuk, D. Jarrett, & M. van der Schaar The Medkit-learn(ing) Environment: Medical Decision Modelling through Simulation, Proceedings of the Neural Information Processing Systems (NeurIPS) track on Datasets and Benchmarks, 2021. PDF
  10. A. J. Chan & M. van der Schaar Scalable Bayesian Inverse Reinforcement Learning, International Conference on Learning Representations (ICLR), 2021. PDF
  11. A. M. Alaa, A. J. Chan, & M. van der Schaar Generative Time-series Modeling with Fourier Flows, International Conference on Learning Representations (ICLR), 2021. PDF
  12. A. J. Chan, A. M. Alaa, Z. Qian, & M. van der Schaar Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift, International Conference on Machine Learning (ICML), 2020. PDF
  13. A. J. Chan & M. van der Schaar Interpretable Policy Learning, MPhil Machine Learning and Machine Intelligence Thesis, 2020.
  14. A. J. Chan & R. Silva Probabilistic Deep Learning, BSc Statistics Thesis, 2019.