10 citations · 21 across the 7 of their papers we have counts for
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cs.LG2022★ 2 cited
Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent
Ruqi Zhang, Qiang Liu, Xin T. Tong
Sampling methods, as important inference and learning techniques, are typically designed for unconstrained domains. However, constraints are ubiquitous in machine learning problems…
cs.LG2022★ 1 cited
A Langevin-like Sampler for Discrete Distributions
Ruqi Zhang, Xingchao Liu, Qiang Liu
We propose discrete Langevin proposal (DLP), a simple and scalable gradient-based proposal for sampling complex high-dimensional discrete distributions. In contrast to Gibbs sampli…
cs.LG2021★ 4 cited
Physics-Guided Discovery of Highly Nonlinear Parametric Partial Differential Equations
Yingtao Luo, Qiang Liu, Yuntian Chen +3
Partial differential equations (PDEs) that fit scientific data can represent physical laws with explainable mechanisms for various mathematically-oriented subjects, such as physics…