2 papers
cs.LG2026
Accurate Large-sample Uncertainty Quantification using Stochastic Gradient Markov Chain Monte Carlo
Yu Wang, Jie Ding, Jonathan H. Huggins
Tuning algorithms such as stochastic gradient descent (SGD) and stochastic gradient Langevin dynamics (SGLD) for approximate sampling and uncertainty quantification remains challen…
stat.ML2026
Variational Smoothing and Inference for SDEs from Sparse Data with Dynamic Neural Flows
Yu Wang, Arnab Ganguly
Stochastic differential equations (SDEs) provide a flexible framework for modeling temporal dynamics in partially observed systems. A central task is to calibrate such models from…