1 citations · 1 across the 14 of their papers we have counts for
16 papers
Denoising Implicit Variational Inference
Zitong Wang, Zekun Wu, Longlin Yu +1
Semi-implicit variational inference (SIVI) expands the representational capacity of variational families, but optimizing the Evidence Lower Bound (ELBO) remains challenging because…
Control Variate Design for Score-Function Gradient Estimators in Importance-Weighted Variational Inference
Kamélia Daudel, Minh-Ngoc Tran, Cheng Zhang
Importance-weighted variational inference approximates intractable densities by optimizing multi-sample Monte Carlo objectives whose tightness increases with the number of importan…
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,…
Variational Autoencoding Discrete Diffusion with Enhanced Dimensional Correlations Modeling
Tianyu Xie, Shuchen Xue, Zijin Feng +4
Discrete diffusion models have recently shown great promise for modeling complex discrete data, with masked diffusion models (MDMs) offering a compelling trade-off between quality…
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…
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…
Hierarchical Semi-Implicit Variational Inference with Application to Diffusion Model Acceleration
Longlin Yu, Tianyu Xie, Yu Zhu +3
Semi-implicit variational inference (SIVI) has been introduced to expand the analytical variational families by defining expressive semi-implicit distributions in a hierarchical ma…
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…
ARTree: A Deep Autoregressive Model for Phylogenetic Inference
Tianyu Xie, Cheng Zhang
Designing flexible probabilistic models over tree topologies is important for developing efficient phylogenetic inference methods. To do that, previous works often leverage the sim…