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A Kernel Approach for Semi-implicit Variational Inference
Longlin Yu, Ziheng Cheng, Shiyue Zhang +1
Semi-implicit variational inference (SIVI) enhances the expressiveness of variational families through hierarchical semi-implicit distributions, but the intractability of their den…
Functional Gradient Flows for Constrained Sampling
Shiyue Zhang, Longlin Yu, Ziheng Cheng +1
Recently, through a unified gradient flow perspective of Markov chain Monte Carlo (MCMC) and variational inference (VI), particle-based variational inference methods (ParVIs) have…
Semi-Implicit Functional Gradient Flow for Efficient Sampling
Shiyue Zhang, Ziheng Cheng, Cheng Zhang
Particle-based variational inference methods (ParVIs) use nonparametric variational families represented by particles to approximate the target distribution according to the kernel…
Kernel Semi-Implicit Variational Inference
Ziheng Cheng, Longlin Yu, Tianyu Xie +2
Semi-implicit variational inference (SIVI) extends traditional variational families with semi-implicit distributions defined in a hierarchical manner. Due to the intractable densit…
Reflected Flow Matching
Tianyu Xie, Yu Zhu, Longlin Yu +5
Continuous normalizing flows (CNFs) learn an ordinary differential equation to transform prior samples into data. Flow matching (FM) has recently emerged as a simulation-free appro…
Particle-based Variational Inference with Generalized Wasserstein Gradient Flow
Ziheng Cheng, Shiyue Zhang, Longlin Yu +1
Particle-based variational inference methods (ParVIs) such as Stein variational gradient descent (SVGD) update the particles based on the kernelized Wasserstein gradient flow for t…