Wenkai Xu
Wenkai Xu
Postdoc, Department of Statistics, University of Oxford
Verified email at stats.ox.ac.uk
Cited by
Cited by
A linear-time kernel goodness-of-fit test
W Jitkrittum, W Xu, Z Szabó, K Fukumizu, A Gretton
arXiv preprint arXiv:1705.07673, 2017
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
A Stein Goodness-of-fit Test for Directional Distributions
W Xu, T Matsuda
AISTATS2020, 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
Model reuse with reduced kernel mean embedding specification
XZ Wu, W Xu, S Liu, ZH Zhou
IEEE Transactions on Knowledge and Data Engineering, 2021
A Stein Goodness-of-test for Exponential Random Graph Models
W Xu, G Reinert
International Conference on Artificial Intelligence and Statistics, 415-423, 2021
Interpretable Stein Goodness-of-fit Tests on Riemannian Manifolds
W Xu, T Matsuda
ICML2021, 2021
A Stein Goodness of fit Test for Exponential Random Graph Models
W Xu, G Reinert
AISTATS2021, 2021
Generalised Kernel Stein Discrepancy (GKSD): A Unifying Approach for Non-parametric Goodness-of-fit Testing
W Xu
arXiv preprint arXiv:2106.12105, 2021
Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data
F Liu, W Xu, J Lu, DJ Sutherland
arXiv preprint arXiv:2106.07636, 2021
A kernel test for quasi-independence
T Fernández, W Xu, M Ditzhaus, A Gretton
NeurIPS 2020, 2020
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data
T Fernandez Aguilar, A Gretton, N Rivera, W XU
International Machine Learning Society (IMLS), 2020
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
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