News:

28-08-2026: Our paper, “IntervalGP-VAE: Uncertainty-Aware Individual Treatment Effect Estimation via Identifiable Proxy-Based Latent Confounder Recovery,” co-authored with Professor Feng Dong, has been published in Transactions on Machine Learning Research (TMLR). The paper introduces IntervalGP-VAE, a new framework that combines variational autoencoders with Interval Gaussian Processes to recover latent confounders from noisy proxy variables and estimate individual treatment effects with calibrated uncertainty intervals. 

18-06-2025: Our latest paper 'Linear Causal Discovery with Interventional Constraints,' co-authored with Professor Feng Dong, has been published in Machine Learning. The paper introduces causal discovery with a new type of constraint, termed interventional constraints to incorporate qualitative knowledge of causal effects into the learning process.

04-05-2023: I will give an online summer school course on Causal Inference this June and July, at Northwestern Polytechnical University.

12-03-2023: Our paper 'The impact of prior knowledge on causal structure learning,' co-authored with Anthony Constantinou and Neville K Kitson, is accepted by Knowledge and Information Systems.

28-11-2022: After three years, our paper  'A survey of Bayesian Network structure learning' is accepted by Artificial Intelligence Review. Great working experience with Anthony Constantinou!

23-11-2022: After two years, our paper  'Improving Bayesian network structure learning in the presence of measurement error,' first authored by Yang Liu, is accepted by Journal of Machine Learning Research. Yang gave dream performance during his PhD study. 

30-09-2022: Our paper 'Effective and efficient structure learning with pruning and model averaging strategies' has been accepted by International Journal of Approximate Reasoning.