Preprints

• Xu, W. & Reinert, G. (2022) AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators. Submitted
  [Paper]

International Journals & Conferences

• Xu, W. (2022). Standardisation-function Kernel Stein Discrepancy (Sf-KSD): A Unifying View on Kernel Stein Discrepancy for Goodness-of-fit Testing. In Proceedings of 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022).
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• *Liu, F.,*Xu, W., Lu, J., & Sutherland, D. J. (2021). Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data. In Advances in Neural Information Processing Systems (NeurIPS 2021).
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• Xu, W. & Reinert, G. (2021) A Stein Goodness-of-t Test for Exponential Random Graph Models. In Proceedings of 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021).
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• Xu, W. & Matsuda, T. (2021) Interpretable Stein Goodness-of-t Tests on Riemannian Manifolds. In Proceedings of 38th International Conference on Machine Learning (ICML2021).
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• Wu, X. Z., Xu, W., Liu, S., & Zhou, Z. H. (2021). Model Reuse with Reduced Kernel Mean Embedding Specification. IEEE Transactions on Knowledge and Data Engineering, May 2021.
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• Xu, W., Niu, G., Hyvärinen, A., & Sugiyama, M. (2021). Direction Matters: On Influence-Preserving Graph Summarization and Max-cut Principle for Directed Graphs. Neural Computation, vol. 33, no. 8, pp. 2128--2162, 2021
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• Fernandez, T., Xu, W., Ditzhaus, M., & Gretton, A. (2020). A Kernel Test for Quasi-independence. In Advances in Neural Information Processing Systems (NeurIPS 2020). Spotlight Presentation.
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• *Fernandez, T., *Rivera, N., *Xu, W. & Gretton, A. (2020) Kernelized Stein Discrepancy Tests of Goodness-of-t for Time-to-Event Data. In Proceedings of 37th International Conference on Machine Learning (ICML2020).
  [Paper]
• *Liu, F.,*Xu, W., Lu, J., Zhang, G., Gretton, A., & Sutherland, D. J. (2020). Learning Deep Kernels for Non-Parametric Two-Sample Tests. In Proceedings of 37th International Conference on Machine Learning (ICML2020).
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• Xu, W. & Matsuda, T. (2020) A Stein Goodness-of-t Test for Directional Distributions. In Proceedings of 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020).
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• Jitkrittum, W., Xu, W., Szabó, Z., Fukumizu, K., & Gretton, A. (2017). A Linear-time Kernel Goodness-of-t Test. In Advances in Neural Information Processing Systems (NIPS2017).
  The Best Paper Award (There are 3 best paper in NIPS 2017.). [Paper]