8 papers · 1 filter
Importance Weighted Variational Inference without the Reparameterization Trick
Kamélia Daudel, Minh-Ngoc Tran, Cheng Zhang
Importance weighted variational inference (VI) approximates densities known up to a normalizing constant by optimizing bounds that tighten with the number of Monte Carlo samples $N…
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…
Continuous Semi-Implicit Models
Longlin Yu, Jiajun Zha, Tong Yang +4
Semi-implicit distributions have shown great promise in variational inference and generative modeling. Hierarchical semi-implicit models, which stack multiple semi-implicit layers,…
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…
PhyloVAE: Unsupervised Learning of Phylogenetic Trees via Variational Autoencoders
Tianyu Xie, Harry Richman, Jiansi Gao +2
Learning informative representations of phylogenetic tree structures is essential for analyzing evolutionary relationships. Classical distance-based methods have been widely used t…
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…