10 citations · 21 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
Sampling with Mollified Interaction Energy Descent
Lingxiao Li, Qiang Liu, Anna Korba +2
Sampling from a target measure whose density is only known up to a normalization constant is a fundamental problem in computational statistics and machine learning. In this paper,…
Rectified Flow: A Marginal Preserving Approach to Optimal Transport
Qiang Liu
We present a flow-based approach to the optimal transport (OT) problem between two continuous distributions on , of minimizing a transport cost $\mathbb{E}[…
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…