3 citations · 5 across the 11 of their papers we have counts for
6 papers · 1 filter
Rethinking Langevin Thompson Sampling from A Stochastic Approximation Perspective
Weixin Wang, Haoyang Zheng, Guang Lin +2
Langevin-based Thompson Sampling (TS) methods for multi-armed bandits use likelihood gradients to approximate posterior samples when exact sampling is intractable. As new rewards a…
ProTDyn: a foundation Protein language model for Thermodynamics and Dynamics generation
Yikai Liu, Haoyang Zheng, Lining Mao +3
Molecular dynamics (MD) simulation has long been the principal computational tool for exploring protein conformational landscapes and dynamics, but its application is limited by hi…
Ultra-Fast Language Generation via Discrete Diffusion Divergence Instruct
Haoyang Zheng, Xinyang Liu, Cindy Xiangrui Kong +5
Fast and high-quality language generation is the holy grail that people pursue in the age of AI. In this work, we introduce Discrete Diffusion Divergence Instruct (DiDi-Instruct),…
BLADE: Bayesian Langevin Active Discovery with Replica Exchange for Identification of Complex Systems
Cindy Xiangrui Kong, Haoyang Zheng, Guang Lin
Traditional methods for system discovery frequently struggle with efficient data usage and uncertainty quantification. Identifying the governing equations of complex dynamical syst…
Active Learning with Bayesian Multi-Fidelity Laplace Neural Operators for Oscillatory Parametric PDEs
Haoyang Zheng, Bongseok Kim, Guang Lin +1
Surrogate models of parametric dynamical systems are essential for many-query and real-time predictions in engineering applications such as design optimization and digital twins. H…
Exploring Non-Convex Discrete Energy Landscapes: An Efficient Langevin-Like Sampler with Replica Exchange
Haoyang Zheng, Hengrong Du, Ruqi Zhang +1
Gradient-based Discrete Samplers (GDSs) are effective for sampling discrete energy landscapes. However, they often stagnate in complex, non-convex settings. To improve exploration,…