| Learning deep kernels for non-parametric two-sample tests F Liu, W Xu, J Lu, G Zhang, A Gretton, DJ Sutherland International conference on machine learning, 6316-6326, 2020 | 204 | 2020 |
| A linear-time kernel goodness-of-fit test W Jitkrittum, W Xu, Z Szabó, K Fukumizu, A Gretton Advances in neural information processing systems 30, 2017 | 122 | 2017 |
| Model reuse with reduced kernel mean embedding specification XZ Wu, W Xu, S Liu, ZH Zhou IEEE Transactions on Knowledge and Data Engineering 35 (1), 699-710, 2021 | 23 | 2021 |
| Meta two-sample testing: Learning kernels for testing with limited data F Liu, W Xu, J Lu, DJ Sutherland Advances in Neural Information Processing Systems 34, 5848-5860, 2021 | 22 | 2021 |
| A Stein Goodness-of-fit Test for Directional Distributions W Xu, T Matsuda AISTATS2020, 2020 | 21 | 2020 |
| Kernelized Stein discrepancy tests of goodness-of-fit for time-to-event data T Fernandez, N Rivera, W Xu, A Gretton International Conference on Machine Learning, 3112-3122, 2020 | 17 | 2020 |
| A Stein goodness-of-test for exponential random graph models W Xu, G Reinert International Conference on Artificial Intelligence and Statistics, 415-423, 2021 | 14 | 2021 |
| Interpretable Stein Goodness-of-fit Tests on Riemannian Manifolds W Xu, T Matsuda ICML2021, 2021 | 10 | 2021 |
| AgraSSt: Approximate graph Stein statistics for interpretable assessment of implicit graph generators W Xu, GD Reinert Advances in Neural Information Processing Systems 35, 24268-24279, 2022 | 6 | 2022 |
| Standardisation-function kernel Stein discrepancy: A unifying view on kernel Stein discrepancy tests for goodness-of-fit W Xu International Conference on Artificial Intelligence and Statistics, 1575-1597, 2022 | 6 | 2022 |
| A Stein Goodness of fit Test for Exponential Random Graph Models W Xu, G Reinert AISTATS2021, 2021 | 5 | 2021 |
| A kernel test for quasi-independence T Fernández, W Xu, M Ditzhaus, A Gretton NeurIPS 2020, 2020 | 4 | 2020 |
| On RKHS choices for assessing graph generators via kernel Stein statistics M Weckbecker, W Xu, G Reinert arXiv preprint arXiv:2210.05746, 2022 | 3 | 2022 |
| Learning Nonlinear Causal Effect via Kernel Anchor Regression W Shi, W Xu Uncertainty in Artificial Intelligence, 1942-1952, 2023 | 2 | 2023 |
| A kernelised Stein statistic for assessing implicit generative models W Xu, GD Reinert Advances in Neural Information Processing Systems 35, 7277-7289, 2022 | 1 | 2022 |
| Direction Matters: On Influence-Preserving Graph Summarization and Max-cut Principle for Directed Graphs W Xu, G Niu, A Hyvärinen, M Sugiyama Neural Computation, 2019 | 1 | 2019 |
| Wenkai Xu’s contribution to the Discussion of ‘Safe testing’by Grünwald, De Heide and Koolen W Xu Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2024 | | 2024 |
| Split Conformal Prediction under Data Contamination J Clarkson, W Xu, M Cucuringu, G Reinert arXiv preprint arXiv:2407.07700, 2024 | | 2024 |
| SteinGen: Generating Fidelitous and Diverse Graph Samples G Reinert, W Xu arXiv preprint arXiv:2403.18578, 2024 | | 2024 |
| Nonlinear Causal Discovery via Kernel Anchor Regression W Shi, W Xu arXiv preprint arXiv:2210.16775, 2022 | | 2022 |
