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20172025
most citedMessage Passing Stein Variational Gradient Descent

49 citations · 112 across the 10 of their papers we have counts for

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5 papers · 1 filter

stat.ML2020★ 7 cited

Learning Optimal Tree Models Under Beam Search

Jingwei Zhuo, Ziru Xu, Wei Dai +4

Retrieving relevant targets from an extremely large target set under computational limits is a common challenge for information retrieval and recommendation systems. Tree models, w…

stat.ML2019★ 2 cited

Understanding MCMC Dynamics as Flows on the Wasserstein Space

Chang Liu, Jingwei Zhuo, Jun Zhu

It is known that the Langevin dynamics used in MCMC is the gradient flow of the KL divergence on the Wasserstein space, which helps convergence analysis and inspires recent particl…

stat.ML2018★ 41 cited

Understanding and Accelerating Particle-Based Variational Inference

Chang Liu, Jingwei Zhuo, Pengyu Cheng +3

Particle-based variational inference methods (ParVIs) have gained attention in the Bayesian inference literature, for their capacity to yield flexible and accurate approximations.…

stat.ML2017

Learning Random Fourier Features by Hybrid Constrained Optimization

Jianqiao Wangni, Jingwei Zhuo, Jun Zhu

The kernel embedding algorithm is an important component for adapting kernel methods to large datasets. Since the algorithm consumes a major computation cost in the testing phase,…

stat.ML2017★ 49 cited

Message Passing Stein Variational Gradient Descent

Jingwei Zhuo, Chang Liu, Jiaxin Shi +3

Stein variational gradient descent (SVGD) is a recently proposed particle-based Bayesian inference method, which has attracted a lot of interest due to its remarkable approximation…