25 citations · 35 across the 10 of their papers we have counts for
11 papers
Hessian-informed, Coordinate Friendly Hamiltonian Monte Carlo in Linear Time
Son Luu, Nikola Surjanovic, Zuheng Xu +2
Riemannian Hamiltonian Monte Carlo (RHMC) is a promising MCMC methodology thanks to its ability to accommodate position-dependent preconditioning and multi-step proposals. While RH…
Asymptotically exact variational flows via involutive MCMC kernels
Zuheng Xu, Trevor Campbell
Most expressive variational families -- such as normalizing flows -- lack practical convergence guarantees, as their theoretical assurances typically hold only at the intractable g…
Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization
Kyurae Kim, Zuheng Xu, Jacob R. Gardner +1
The performance of sequential Monte Carlo (SMC) samplers heavily depends on the tuning of the Markov kernels used in the path proposal. For SMC samplers with unadjusted Markov kern…
Is Gibbs sampling faster than Hamiltonian Monte Carlo on GLMs?
Son Luu, Zuheng Xu, Nikola Surjanovic +3
The Hamiltonian Monte Carlo (HMC) algorithm is often lauded for its ability to effectively sample from high-dimensional distributions. In this paper we challenge the presumed domin…
Turning Waste into Wealth: Leveraging Low-Quality Samples for Enhancing Continuous Conditional Generative Adversarial Networks
Xin Ding, Yongwei Wang, Zuheng Xu
Continuous Conditional Generative Adversarial Networks (CcGANs) enable generative modeling conditional on continuous scalar variables (termed regression labels). However, they can…
Embracing the chaos: analysis and diagnosis of numerical instability in variational flows
Zuheng Xu, Trevor Campbell
In this paper, we investigate the impact of numerical instability on the reliability of sampling, density evaluation, and evidence lower bound (ELBO) estimation in variational flow…
MixFlows: principled variational inference via mixed flows
Zuheng Xu, Naitong Chen, Trevor Campbell
This work presents mixed variational flows (MixFlows), a new variational family that consists of a mixture of repeated applications of a map to an initial reference distribution. F…
Bayesian inference via sparse Hamiltonian flows
Naitong Chen, Zuheng Xu, Trevor Campbell
A Bayesian coreset is a small, weighted subset of data that replaces the full dataset during Bayesian inference, with the goal of reducing computational cost. Although past work ha…
The computational asymptotics of Gaussian variational inference and the Laplace approximation
Zuheng Xu, Trevor Campbell
Gaussian variational inference and the Laplace approximation are popular alternatives to Markov chain Monte Carlo that formulate Bayesian posterior inference as an optimization pro…
Distilling and Transferring Knowledge via cGAN-generated Samples for Image Classification and Regression
Xin Ding, Yongwei Wang, Zuheng Xu +2
Knowledge distillation (KD) has been actively studied for image classification tasks in deep learning, aiming to improve the performance of a student based on the knowledge from a…
Continuous Conditional Generative Adversarial Networks: Novel Empirical Losses and Label Input Mechanisms
Xin Ding, Yongwei Wang, Zuheng Xu +2
This work proposes the continuous conditional generative adversarial network (CcGAN), the first generative model for image generation conditional on continuous, scalar conditions (…