Edwin Fong

Edwin Fong

Assistant Professor

Publications

  1. E. Fong, A. Yiu, “Asymptotics for parametric martingale posteriors,” arXiv:2410.17692. 2024.
  2. Y. McLatchie, E. Fong, D. T. Frazier, and J. Knoblauch, “Predictive performance of power posteriors,” arXiv:2408.08806. 2024.
  3. E. Fong, A. Yiu, “Bayesian Quantile Estimation and Regression with Martingale Posteriors,” arXiv:2406.03358. 2024.
  4. E. Dimitriou, E. Fong, K. Diaz-Ordaz, B. Lehmann, “Data Fusion for Heterogeneous Treatment Effect Estimation with Multi-Task Gaussian Processes,” arXiv:2405.20957. 2024.
  5. H. Lee, G. Nam, E. Fong, and J. Lee, “Enhancing Transfer Learning with Flexible Nonparametric Posterior Sampling,” 12th International Conference on Learning Representations (ICLR), 2024.
  6. A. Yiu, E. Fong, C. Holmes, and J. Rousseau, “Semiparametric posterior corrections,” arXiv:2306.06059. 2023.
  7. L. E. Dang, E. Fong, J. M. Tarp, K. K. B. Clemmensen, H. Ravn, K. Kvist, J. B. Buse, M. van der Laan, and M. Petersen, “A causal roadmap for hybrid randomized and real-world data designs: Case study of Semaglutide and cardiovascular outcomes,” arXiv:2305.07647. 2023.
  8. E. Fong, C. Holmes, and S. G. Walker, “Martingale posterior distributions,” Journal of the Royal Statistical Society: Series B (with discussion), 2023.
  9. S. Ghalebikesabi, C. Holmes, E. Fong, and B. Lehmann, “Quasi-Bayesian nonparametric density estimation via autoregressive predictive updates,” 39th Conference on Uncertainty in Artificial Intelligence (UAI), 2023. Spotlight.
  10. H. Lee, E. Yun, G. Nam, E. Fong, and J. Lee, “Martingale posterior neural processes,” 11th International Conference on Learning Representations (ICLR), 2023. Notable top 25%.
  11. A. Yiu, E. Fong, S. Walker, and C. Holmes, “Causal predictive inference and target trial emulation,” arXiv:2207.12479, 2022.
  12. Z. Tsangalidou, E. Fong, J. Sundgaard, T. Abrahamsen, and K. Kvist, “Multimodal deep transfer learning for the analysis of optical coherence tomography scans and retinal fundus photographs,” NeurIPS 2022 Workshop on Learning Meaningful Representations of Life, 2022.
  13. E. Fong, “The predictive view of Bayesian inference,” PhD thesis, University of Oxford, 2022.
  14. E. Fong and B. Lehmann, “A Predictive Approach to Bayesian Nonparametric Survival Analysis,” 25th International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.
  15. E. Fong and C. Holmes, “Conformal Bayesian Computation,” in Thirty-Fifth Conference on Neural Information Processing Systems (NeurIPS), 2021.
  16. E. Fong and C. Holmes, “On the marginal likelihood and cross-validation,” Biometrika, vol. 107, no. 2, pp. 489–496, 2020.
  17. E. Fong, S. Lyddon, and C. Holmes, “Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap,” in Proceedings of the 36th International Conference on Machine Learning (ICML), 2019. Oral (long).