Zuheng(David) Xu

A non-equilibrium random walker ///

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I am a Ph.D candidate in Statistics at University of British Columbia (UBC), under the supervision of Trevor Campbell. My research focuses on probabilistic ML and generative modelling, particularly on the scalable (approximate) sampling methods (VI/MCMC/SMC/etc.) with guarantees. I also interned with Jason Hartford at Valence Labs, where I developed my interest in causal representation learning and the general applications in biology and drug discovery.

A fun fact about my research: After ICLR 2025, the average rejection rate of the research projects I lead is ~1/3---clearly indicating that I'm working in the right field!

I'm also part of the Turing.jl community, which has kindly included me as a member despite my minuscule contribution. Check out NormalizingFlows.jl if you are looking for a simple yet flexible normalizing flow package in Julia that is suited for approximate Bayesian inference.

Preprints and Workshops

Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization
Kyurae Kim*, Zuheng Xu*, Jacob R. Gardner, Trevor Campbell
[arXiv]
Automated Discovery of Pairwise Interactions from Unstructured Data
Zuheng Xu*, Moksh Jain*, Ali Denton, Shawn Whitfield, Aniket Didolkar, Berton Earnshaw, Jason Hartford
[arXiv]
Score-Based Interaction Testing in Pairwise Experiments
Jana Osea*, Zuheng Xu*, Cian Eastwood, Jason Hartford
Causal Representation Learning Workshop @NeurIPS, 2024
[CRL@NeurIPS]   [poster]

Publication

Is Gibbs sampling faster than Hamiltonian Monte Carlo on GLMs?
Son Luu, Zuheng Xu, Nikola Surjanovic, Miguel Biron-Lattes, Trevor Campbell, Alexandre Bouchard-Côté
International Conference on Artificial Intelligence and Statistics, 2025
[arXiv]   [AISTATS]
Propensity Score Alignment of Unpaired Multimodal Data
Johnny Xi, Jana Osea, Zuheng Xu, Jason Hartford
Advances in Neural Information Processing Systems, 2024
[arXiv]   [NeurIPS]
Turning waste into wealth: leveraging low-quality samples for enhancing continuous conditional generative adversarial networks
Xin Ding, Yongwei Wang, Zuheng Xu
AAAI Conference on Artificial Intelligence, 2024
[arXiv]   [AAAI]
Embracing the chaos: analysis and diagnosis of numerical instability in variational flows
Zuheng Xu, Trevor Campbell
Advances in Neural Information Processing Systems, 2023
[arXiv]   [NeurIPS]   [poster]   [slides]
MixFlows: principled variational inference via mixed flows
Zuheng Xu, Naitong Chen, Trevor Campbell
International Conference on Machine Learning, 2023
[arXiv]   [code]   [ICML]   [poster]
Bayesian inference via sparse Hamiltonian flows
Naitong Chen, Zuheng Xu, Trevor Campbell
Advances in Neural Information Processing Systems (oral), 2022
[arXiv]   [code]   [NeurIPS]
Distilling and transferring knowledge via cGAN-generated samples for image classification and regression
Xin Ding, Yongwei Wang, Zuheng Xu, Z. Jane Wang, William J. Welch
Expert Systems with Applications, 2023
[arXiv]   [code]   [ESA]
Continuous conditional generative adversarial networks: novel empirical losses and label input mechanisms
Xin Ding, Yongwei Wang, Zuheng Xu, Z. Jane Wang, William J. Welch
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
[arXiv]   [code]   [TPAMI]
The computational asymptotics of Gaussian variational inference and the Laplace approximation
Zuheng Xu, Trevor Campbell
Statistics and Computing 32(63), 2022
[arXiv]   [code]   [Statistics and Computing]   [Msc. Thesis]   [slides]
CcGAN: continuous conditional generative adversarial networks for image generation
Xin Ding, Yongwei Wang, Zuheng Xu, William J. Welch, Z. Jane Wang
International Conference on Learning Representations, 2021
[ICLR]   [code]
Bayesian pseudocoresets
Dionysis Manousakas, Zuheng Xu, Cecilia Mascolo, Trevor Campbell
Advances in Neural Information Processing Systems, 2020
[NeurIPS]   [code]
Student submission patterns in online homework and relationships to learning outcomes: a pilot study
Gianni Co, Zuheng Xu, Giorgio Sgarbi, Siqi Cheng, Ziqi Xu, Agnes d'Entremont, Juan Abelló
Proceedings of the Canadian Engineering Education Association Conference, 2019
[CEEA]

Teaching

    Teaching assistant of the Department of Statistics (UBC)
  • STAT 306 (Finding Relationship in Data) 2024 Fall
  • STAT 305 (Intro. to Stat. Inference) 2023 Spring
  • STAT 200 (Elementary Stat.)---head TA 2022 Spring
  • STAT 404 (Design of Experiment) 2021 Fall
  • STAT 302 (Intro. to Prob.) 2021 Spring
  • STAT 406 (Methods for Stat. Learning)---head TA 2020 Fall
  • STAT 461/561 (Stat. Theory II)---grad. mandatory course 2020 Spring
  • STAT 200 (Elementary Stat.) 2020 Spring
  • STAT 200 (Elementary Stat.) 2019 Spring
  • STAT 300 (Intermediate Stat.) 2018 Fall
    • Teaching assistant of School of Science (UBC)
  • SCIE 300 (Communicating Science) 2019 Fall

Honors/Awards

  • Marshall Prize (UBC Stat.)
    2024
  • Top Reviewer Award (NeurIPS)
    2023
  • Statistics Graduate Teaching Assistant Awards (UBC Stat.)
    2022
  • SBSS Student Paper Award (AMS)
    Topic contributed talk at JSM 2022
    2022
  • Four-year Fellowship (FYF) For PhD students (UBC)
    Four-year Fellowship (4YF) Tuition Award (UBC)
    2020-2024
  • Faculty of Science Graduate Award (UBC) 2020-2023
  • Excellence Initiaitive PhD Award (UBC) 2020-2021
  • Meritorious Winner by COMAP (MCM) 2017

Education

  • Ph.D. of Statistics, University of British Columbia 2020-present
  • M.Sc. of Statistics, University of British Columbia 2018-2020
  • B.Sc. of Statistics, Sichuan University 2014-2018