Preprints

• Reinert, G, W. Xu, SteinGen: Generating Fidelitous and Diverse Graph Samples. April 2024.
  [Arxiv][Code]

International Journals & Conferences

• Clarkson, J., Xu, W., Cucuringu, M. & Reinert, G. “Split Conformal Prediction under Data Contamination.” Conformal and Probabilistic Prediction with Applications (COPA). (2024) [Arxiv]
  
• Xu, W. “Wenkai Xu’s contribution to the Discussion of ‘Safe Testing’by Peter Grünwald, Rianne de Heide, and Wouter M. Koolen.” Journal of the Royal Statistical Society Series B: Statistical Methodology. (2024)
  
• Shi, W. & Xu, W. “Learning Causal Effect via Kernel Anchor Regression.” Conference on Uncertainty in Artificial Intelligence (UAI). (2023)
• Xu, W. & Reinert, G. “A Kernelised Stein Statistic for Assessing Implicit Generative Models.” Advances in Neural Information Processing Systems (NeurIPS). (2022)
• Xu, W. & Reinert, G. “AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators .” Advances in Neural Information Processing Systems (NeurIPS). (2022)
• Weckbecker, M., Xu, W. & Reinert, G. “On RKHS Choices for Assessing Graph Generators via Kernel Stein Statistics.” In NeurIPS Score-Based Method Workshop. (2022)
• Xu, W. “Standardisation-function kernel Stein discrepancy(Sf -KSD): a unifying view on Kernel Stein Discrepancy for non-parametric goodness-of-fit tests.” International Conference on Artificial Intelligence and Statistics (AISTATS). (2022)
  
• Liu, F.*, Xu, W.*, Lu, J., & Sutherland, D. “Meta Two-Sample Testing.” Advances in Neural Information Processing Systems (NeurIPS). (2021)
• Xu, W. & Matsuda, T. “Interpretable Stein goodness-of-fit tests for Riemannian manifold.” International Conference on Machine Learning (ICML). (2021)
• Xu, W. & Reinert, G. “A Stein Goodness-of-fit Test for Exponential Random Graph Model.” International Conference on Artificial Intelligence and Statistics (AISTATS). (2021)
• Wu, X., Xu, W., Liu, S. & Zhou, Z. “Model Reuse with Reduced Kernel Mean Embedding Specification.” IEEE Transactions on Knowledge and Data Engineering (TKDE). (2021)
• Xu, W. & Matsuda, T. “On Geometry of Stein Goodness-of-fit Testing.” International Conference on Geometric Science of Information (GSI). (2021)
• Xu, W., Niu, G., Hyvarinen, A. & Sugiyama, M. “Direction Matters: On Influence-Preserving Graph Summarization and Max-cut Principle for Directed Graphs.” Neural Computation (2021)
• Fernandez, T.*, Xu, W.*, Marc Ditzhaus & Gretton, A. “A kernel quasi-indepdence test.” Advances in Neural Information Processing Systems (NeurIPS). (2020)
• Xu, W. & Matsuda, T. “A Stein Goodness-of-fit Test for Directional Distributions.” International Conference on Artificial Intelligence and Statistics (AISTATS). (2020)
• Fernandez, T.*, Rivera, N.*, Xu, W.* & Gretton, A. “Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to- Event Data.” International Conference on Machine Learning (ICML). (2020)
• Liu, F.*, Xu, W.*, Lu, J., Zhang, G., Gretton, A. & Sutherland, D. “Learning Deep Kernels for Non-Parametric Two-Sample Tests.” International Conference on Machine Learning (ICML). (2020)
• Xu, W., Niu, G., Hyvarinen, A. & Sugiyama, M. “Influence-Preserving Classification for Directed Graphs.” ACML Weakly- supervised Learning Workshop. (2019)
• Xu, W. & Hyvarinen, A. “Causal Clustering Detection via Complex Eigen-decomposition.” ICML Workshop on CausalML. (2018)
• Jitkrittum, W., Xu, W., Szabó, Z., Fukumizu, K. & Gretton, A. “A Linear-time Kernel Goodness-of-fit Test.” Advances in Neural Information Processing Systems (NeurIPS). (2017) (The Best Paper Award)

Thesis and Technical Reports

• W. Xu. Advances in Non-parametric Hypothesis Testing with Kernels. UCL 2021.