224 citations · 3.2k across the 170 of their papers we have counts for
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stat.ML2019★ 4 cited
Scalable Training of Inference Networks for Gaussian-Process Models
Jiaxin Shi, Mohammad Emtiyaz Khan, Jun Zhu
Inference in Gaussian process (GP) models is computationally challenging for large data, and often difficult to approximate with a small number of inducing points. We explore an al…
stat.ML2019★ 14 cited
Function Space Particle Optimization for Bayesian Neural Networks
Ziyu Wang, Tongzheng Ren, Jun Zhu +1
While Bayesian neural networks (BNNs) have drawn increasing attention, their posterior inference remains challenging, due to the high-dimensional and over-parameterized nature. To…