Delu Zeng

Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian Processes

2023 · arXiv (Cornell University)

Deep Gaussian Process (DGP) models offer a powerful nonparametric approach for Bayesian inference, but exact inference is typically intractable, motivating the use of various approximations. However, existing approaches, such as mean-field Gaussian assumptions, limit the …

Diffusion Bridge Variational Inference for Deep Gaussian Processes

2025 · arXiv (Cornell University)

Deep Gaussian processes (DGPs) enable expressive hierarchical Bayesian modeling but pose substantial challenges for posterior inference, especially over inducing variables. Denoising diffusion variational inference (DDVI) addresses this by modeling the posterior as a time-reversed diffusion …