Wenkai Xu
Wenkai Xu
Postdoc, Foundations of Machine Learning Systems group, Tuebingen AI Center
Verified email at - Homepage
Cited by
Cited by
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 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
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
A Stein Goodness-of-fit Test for Directional Distributions
W Xu, T Matsuda
AISTATS2020, 2020
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
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
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
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
A Stein Goodness of fit Test for Exponential Random Graph Models
W Xu, G Reinert
AISTATS2021, 2021
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
A kernel test for quasi-independence
T Fernández, W Xu, M Ditzhaus, A Gretton
NeurIPS 2020, 2020
On RKHS choices for assessing graph generators via kernel Stein statistics
M Weckbecker, W Xu, G Reinert
arXiv preprint arXiv:2210.05746, 2022
Learning Nonlinear Causal Effect via Kernel Anchor Regression
W Shi, W Xu
Uncertainty in Artificial Intelligence, 1942-1952, 2023
A kernelised Stein statistic for assessing implicit generative models
W Xu, GD Reinert
Advances in Neural Information Processing Systems 35, 7277-7289, 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
SteinGen: Generating Fidelitous and Diverse Graph Samples
G Reinert, W Xu
arXiv preprint arXiv:2403.18578, 2024
On RKHS Choices for Assessing Graph Generators via Kernel Stein Statistics
W Xu, G Reinert, M Weckbecker
NeurIPS 2022 Workshop on Score-Based Methods, 2022
Nonlinear Causal Discovery via Kernel Anchor Regression
W Shi, W Xu
arXiv preprint arXiv:2210.16775, 2022
Advances in Non-parametric Hypothesis Testing with Kernels
W Xu
UCL (University College London), 2021
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