Scholar
Zuheng(David) Xu
Google Scholar ID: lkMkblkAAAAJ
PhD student, Department of Statistics, University of British Columnbia
Citations & Impact
Citations
298
H-index
7
i10-index
7
Publications
20
Co-authors
6
list available
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- - Preprints and Workshops: 'Automated Discovery of Pairwise Interactions from Unstructured Data', 'Score-Based Interaction Testing in Pairwise Experiments'
- - Publications: 'Asymptotically exact variational flows via involutive MCMC kernels', 'Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization', 'Is Gibbs sampling faster than Hamiltonian Monte Carlo on GLMs?', 'Propensity Score Alignment of Unpaired Multimodal Data', 'Turning waste into wealth: leveraging low-quality samples for enhancing continuous conditional generative adversarial networks', 'Embracing the chaos: analysis and diagnosis of numerical instability in variational flows', 'MixFlows: principled variational inference via mixed flows', 'Bayesian inference via sparse Hamiltonian flows', 'Distilling and transferring knowledge via cGAN-generated samples for image classification and regression', 'Continuous conditional generative adversarial networks: novel empirical losses and label input mechanisms', 'The computational asymptotics of Gaussian variational inference and the Laplace approximation', 'CcGAN: continuous conditional generative adversarial networks for image generation'
Research Experience
- - Interned at Valence Labs, developing interest in causal representation learning and its applications in biology and drug discovery.
Education
- - Degree: Ph.D. Candidate
- - Institution: University of British Columbia (UBC)
- - Advisor: Trevor Campbell
- - Time: Not specified
- - Major: Statistics
Background
- - Research Interests: Probabilistic ML, Generative Modeling, particularly scalable (approximate) sampling methods (VI/MCMC/SMC/etc.) with guarantees.
- - Professional Field: Statistics
- - Brief Introduction: Ph.D. candidate in Statistics at the University of British Columbia (UBC), supervised by Trevor Campbell. Interned at Valence Labs with Jason Hartford, where he developed an interest in causal representation learning and its applications in biology and drug discovery.
Miscellany
- - Member of the Turing.jl community, despite minimal contributions.
- - Recommends NormalizingFlows.jl, a simple yet flexible normalizing flow package in Julia suitable for approximate Bayesian inference.
