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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…
Diffusion-PINN Sampler
Zhekun Shi, Longlin Yu, Tianyu Xie +1
Recent success of diffusion models has inspired a surge of interest in developing sampling techniques using reverse diffusion processes. However, accurately estimating the drift te…
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…