6 papers
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,…
Adversarial Transform Particle Filters
Chengxin Gong, Wei Lin, Cheng Zhang
The particle filter (PF) and the ensemble Kalman filter (EnKF) are widely used for approximate inference in state-space models. From a Bayesian perspective, these algorithms repres…
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…
Provable Sample-Efficient Transfer Learning Conditional Diffusion Models via Representation Learning
Ziheng Cheng, Tianyu Xie, Shiyue Zhang +1
While conditional diffusion models have achieved remarkable success in various applications, they require abundant data to train from scratch, which is often infeasible in practice…