45 citations · 59 across the 11 of their papers we have counts for
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stat.ML2023
STANLEY: Stochastic Gradient Anisotropic Langevin Dynamics for Learning Energy-Based Models
Belhal Karimi, Jianwen Xie, Ping Li
We propose in this paper, STANLEY, a STochastic gradient ANisotropic LangEvin dYnamics, for sampling high dimensional data. With the growing efficacy and potential of Energy-Based…
stat.ML2023
A Tale of Two Latent Flows: Learning Latent Space Normalizing Flow with Short-run Langevin Flow for Approximate Inference
Jianwen Xie, Yaxuan Zhu, Yifei Xu +2
We study a normalizing flow in the latent space of a top-down generator model, in which the normalizing flow model plays the role of the informative prior model of the generator. W…
stat.ML2023
Exploration in Model-based Reinforcement Learning with Randomized Reward
Lingxiao Wang, Ping Li
Model-based Reinforcement Learning (MBRL) has been widely adapted due to its sample efficiency. However, existing worst-case regret analysis typically requires optimistic planning,…