Bayesian Pseudocoresets. - Dionysis Manousakas; Zuheng Xu; Cecilia Mascolo; Trevor Campbell (NeurIPS, 2020)

CcGAN: Continuous Conditional Generative Adversarial Networks for Image Generation. - Xin Ding; Yongwei Wang; Zuheng Xu; William J. Welch; Z. Jane Wang 001 (ICLR, 2021)

Bayesian inference via sparse Hamiltonian flows. - Naitong Chen; Zuheng Xu; Trevor Campbell (NeurIPS, 2022)

Ergodic variational flows. - Zuheng Xu; Naitong Chen; Trevor Campbell (CoRR, 2022)

The computational asymptotics of Gaussian variational inference and the Laplace approximation. - Zuheng Xu; Trevor Campbell (Stat. Comput., 2022)

MixFlows: principled variational inference via mixed flows. - Zuheng Xu; Naitong Chen; Trevor Campbell (ICML, 2023)

Embracing the chaos: analysis and diagnosis of numerical instability in variational flows. - Zuheng Xu; Trevor Campbell (NeurIPS, 2023)

Distilling and transferring knowledge via cGAN-generated samples for image classification and regression. - Xin Ding; Yongwei Wang; Zuheng Xu; Z. Jane Wang 001; William J. Welch (Expert Syst. Appl., 2023)

Continuous Conditional Generative Adversarial Networks: Novel Empirical Losses and Label Input Mechanisms. - Xin Ding; Yongwei Wang; Zuheng Xu; William J. Welch; Z. Jane Wang 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Turning Waste into Wealth: Leveraging Low-Quality Samples for Enhancing Continuous Conditional Generative Adversarial Networks. - Xin Ding; Yongwei Wang; Zuheng Xu (AAAI, 2024)

Propensity Score Alignment of Unpaired Multimodal Data. - Johnny Xi; Jana Osea; Zuheng Xu; Jason S. Hartford (NeurIPS, 2024)

Automated Discovery of Pairwise Interactions from Unstructured Data. - Zuheng Xu; Moksh Jain; Ali Denton; Shawn Whitfield; Aniket Didolkar; Berton Earnshaw; Jason S. Hartford (CoRR, 2024)

Is Gibbs sampling faster than Hamiltonian Monte Carlo on GLMs? - Son Luu; Zuheng Xu; Nikola Surjanovic; Miguel Biron-Lattes; Trevor Campbell; Alexandre Bouchard-Côté (AISTATS, 2025)

Recent Advances, Applications and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2024 Symposium. - Amin Adibi; Xu Cao; Zongliang Ji; Jivat Neet Kaur; Winston Chen; Elizabeth Healey; Brighton Nuwagira; Wenqian Ye; Geoffrey Woollard; Maxwell A. Xu; Hejie Cui; Johnny Xi; et al. (CoRR, 2025)

Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization. - Kyurae Kim; Zuheng Xu; Jacob R. Gardner; Trevor Campbell (CoRR, 2025)