Search Results for author: Qinsheng Zhang

Found 11 papers, 6 papers with code

gDDIM: Generalized denoising diffusion implicit models

1 code implementation11 Jun 2022 Qinsheng Zhang, Molei Tao, Yongxin Chen

Our goal is to extend the denoising diffusion implicit model (DDIM) to general diffusion models~(DMs).

Denoising

Fast Sampling of Diffusion Models with Exponential Integrator

1 code implementation29 Apr 2022 Qinsheng Zhang, Yongxin Chen

Our goal is to develop a fast sampling method for DMs with a much less number of steps while retaining high sample quality.

Variational Wasserstein gradient flow

1 code implementation4 Dec 2021 Jiaojiao Fan, Qinsheng Zhang, Amirhossein Taghvaei, Yongxin Chen

Wasserstein gradient flow has emerged as a promising approach to solve optimization problems over the space of probability distributions.

Path Integral Sampler: a stochastic control approach for sampling

1 code implementation ICLR 2022 Qinsheng Zhang, Yongxin Chen

The PIS is built on the Schr\"odinger bridge problem which aims to recover the most likely evolution of a diffusion process given its initial distribution and terminal distribution.

Diffusion Normalizing Flow

1 code implementation NeurIPS 2021 Qinsheng Zhang, Yongxin Chen

Our method is closely related to normalizing flow and diffusion probabilistic models and can be viewed as a combination of the two.

Density Estimation Image Generation

Learning Hidden Markov Models from Aggregate Observations

no code implementations23 Nov 2020 Rahul Singh, Qinsheng Zhang, Yongxin Chen

This problem arises when only the population level counts of the number of individuals at each time step are available, from which one seeks to learn the individual hidden Markov model.

Filtering for Aggregate Hidden Markov Models with Continuous Observations

no code implementations4 Nov 2020 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).

Incremental inference of collective graphical models

no code implementations26 Jun 2020 Rahul Singh, Isabel Haasler, Qinsheng Zhang, Johan Karlsson, Yongxin Chen

We consider incremental inference problems from aggregate data for collective dynamics.

Multi-marginal optimal transport and probabilistic graphical models

3 code implementations25 Jun 2020 Isabel Haasler, Rahul Singh, Qinsheng Zhang, Johan Karlsson, Yongxin Chen

We study multi-marginal optimal transport problems from a probabilistic graphical model perspective.

Bayesian Inference

Improving Robustness via Risk Averse Distributional Reinforcement Learning

no code implementations L4DC 2020 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, of the trained policies.

Distributional Reinforcement Learning reinforcement-learning

Inference with Aggregate Data: An Optimal Transport Approach

no code implementations31 Mar 2020 Rahul Singh, Isabel Haasler, Qinsheng Zhang, Johan Karlsson, Yongxin Chen

Consequently, the celebrated Sinkhorn/iterative scaling algorithm for multi-marginal optimal transport can be leveraged together with the standard belief propagation algorithm to establish an efficient inference scheme which we call Sinkhorn belief propagation (SBP).

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