223 citations · 360 across the 19 of their papers we have counts for
19 papers
Direct Discriminative Optimization: Your Likelihood-Based Visual Generative Model is Secretly a GAN Discriminator
Kaiwen Zheng, Yongxin Chen, Huayu Chen +4
While likelihood-based generative models, particularly diffusion and autoregressive models, have achieved remarkable fidelity in visual generation, the maximum likelihood estimatio…
A parallel framework for graphical optimal transport
Jiaojiao Fan, Isabel Haasler, Qinsheng Zhang +2
We study multi-marginal optimal transport (MOT) problems where the underlying cost has a graphical structure. These graphical multi-marginal optimal transport problems have found a…
RefDrop: Controllable Consistency in Image or Video Generation via Reference Feature Guidance
Jiaojiao Fan, Haotian Xue, Qinsheng Zhang +1
There is a rapidly growing interest in controlling consistency across multiple generated images using diffusion models. Among various methods, recent works have found that simply m…
Improved Order Analysis and Design of Exponential Integrator for Diffusion Models Sampling
Qinsheng Zhang, Jiaming Song, Yongxin Chen
Efficient differential equation solvers have significantly reduced the sampling time of diffusion models (DMs) while retaining high sampling quality. Among these solvers, exponenti…
DiffCollage: Parallel Generation of Large Content with Diffusion Models
Qinsheng Zhang, Jiaming Song, Xun Huang +2
We present DiffCollage, a compositional diffusion model that can generate large content by leveraging diffusion models trained on generating pieces of the large content. Our approa…
An optimal control approach to particle filtering on Lie groups
Bo Yuan, Qinsheng Zhang, Yongxin Chen
We study the filtering problem over a Lie group that plays an important role in robotics and aerospace applications. We present a new particle filtering algorithm based on stochast…
eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers
Yogesh Balaji, Seungjun Nah, Xun Huang +10
Large-scale diffusion-based generative models have led to breakthroughs in text-conditioned high-resolution image synthesis. Starting from random noise, such text-to-image diffusio…
gDDIM: Generalized denoising diffusion implicit models
Qinsheng Zhang, Molei Tao, Yongxin Chen
Our goal is to extend the denoising diffusion implicit model (DDIM) to general diffusion models~(DMs) besides isotropic diffusions. Instead of constructing a non-Markov noising pro…
Fast Sampling of Diffusion Models with Exponential Integrator
Qinsheng Zhang, Yongxin Chen
The past few years have witnessed the great success of Diffusion models~(DMs) in generating high-fidelity samples in generative modeling tasks. A major limitation of the DM is its…
Variational Wasserstein gradient flow
Jiaojiao Fan, Qinsheng Zhang, Amirhossein Taghvaei +1
Wasserstein gradient flow has emerged as a promising approach to solve optimization problems over the space of probability distributions. A recent trend is to use the well-known JK…
Path Integral Sampler: a stochastic control approach for sampling
Qinsheng Zhang, Yongxin Chen
We present Path Integral Sampler~(PIS), a novel algorithm to draw samples from unnormalized probability density functions. The PIS is built on the Schrödinger bridge problem which…
Diffusion Normalizing Flow
Qinsheng Zhang, Yongxin Chen
We present a novel generative modeling method called diffusion normalizing flow based on stochastic differential equations (SDEs). The algorithm consists of two neural SDEs: a forw…
An optimal control approach to particle filtering
Qinsheng Zhang, Amirhossein Taghvaei, Yongxin Chen
We present a novel particle filtering framework for continuous-time dynamical systems with continuous-time measurements. Our approach is based on the duality between estimation and…
Filtering for Aggregate Hidden Markov Models with Continuous Observations
Qinsheng Zhang, Rahul Singh, Yongxin Chen
We consider a class of filtering problems for large populations where each individual is modeled by the same hidden Markov model (HMM). In this paper, we focus on aggregate inferen…
Learning Hidden Markov Models from Aggregate Observations
Rahul Singh, Qinsheng Zhang, Yongxin Chen
In this paper, we propose an algorithm for estimating the parameters of a time-homogeneous hidden Markov model from aggregate observations. This problem arises when only the popula…
Incremental inference of collective graphical models
Rahul Singh, Isabel Haasler, Qinsheng Zhang +2
We consider incremental inference problems from aggregate data for collective dynamics. In particular, we address the problem of estimating the aggregate marginals of a Markov chai…
Multi-marginal optimal transport and probabilistic graphical models
Isabel Haasler, Rahul Singh, Qinsheng Zhang +2
We study multi-marginal optimal transport problems from a probabilistic graphical model perspective. We point out an elegant connection between the two when the underlying cost for…
Improving Robustness via Risk Averse Distributional Reinforcement Learning
Rahul Singh, Qinsheng Zhang, Yongxin Chen
One major obstacle that precludes the success of reinforcement learning in real-world applications is the lack of robustness, either to model uncertainties or external disturbances…
Inference with Aggregate Data: An Optimal Transport Approach
Rahul Singh, Isabel Haasler, Qinsheng Zhang +2
We consider inference (filtering) problems over probabilistic graphical models with aggregate data generated by a large population of individuals. We propose a new efficient belief…