44 citations · 117 across the 31 of their papers we have counts for
7 papers · 1 filter
Convergence of Unadjusted Langevin in High Dimensions: Delocalization of Bias
Yifan Chen, Xiaoou Cheng, Jonathan Niles-Weed +1
The unadjusted Langevin algorithm is commonly used to sample probability distributions in extremely high-dimensional settings. However, existing analyses of the algorithm for stron…
Fisher-Rao Gradient Flow: Geodesic Convexity and Functional Inequalities
José A. Carrillo, Yifan Chen, Daniel Zhengyu Huang +2
The dynamics of probability density functions have been extensively studied in computational science and engineering to understand physical phenomena and facilitate algorithmic des…
Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient Flows
Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang +2
In this paper, we study efficient approximate sampling for probability distributions known up to normalization constants. We specifically focus on a problem class arising in Bayesi…
Gaussian Measures Conditioned on Nonlinear Observations: Consistency, MAP Estimators, and Simulation
Yifan Chen, Bamdad Hosseini, Houman Owhadi +1
The article presents a systematic study of the problem of conditioning a Gaussian random variable on nonlinear observations of the form where $ϕ: \mathcal{X} \to…
Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors
Zihui Wu, Yu Sun, Yifan Chen +3
Diffusion models (DMs) have recently shown outstanding capabilities in modeling complex image distributions, making them expressive image priors for solving Bayesian inverse proble…
Sequential-in-time training of nonlinear parametrizations for solving time-dependent partial differential equations
Huan Zhang, Yifan Chen, Eric Vanden-Eijnden +1
Sequential-in-time methods solve a sequence of training problems to fit nonlinear parametrizations such as neural networks to approximate solution trajectories of partial different…
Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
Yifan Chen, Mark Goldstein, Mengjian Hua +3
We propose a framework for probabilistic forecasting of dynamical systems based on generative modeling. Given observations of the system state over time, we formulate the forecasti…