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Found 11 papers, 6 papers with code
gDDIM: Generalized denoising diffusion implicit models
Our goal is to extend the denoising diffusion implicit model (DDIM) to general diffusion models~(DMs).
Fast Sampling of Diffusion Models with Exponential Integrator
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
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
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
Our method is closely related to normalizing flow and diffusion probabilistic models and can be viewed as a combination of the two.
Learning Hidden Markov Models from Aggregate Observations
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
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
We consider incremental inference problems from aggregate data for collective dynamics.
Multi-marginal optimal transport and probabilistic graphical models
We study multi-marginal optimal transport problems from a probabilistic graphical model perspective.
Improving Robustness via Risk Averse Distributional Reinforcement Learning
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
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).
