131 citations · 164 across the 8 of their papers we have counts for
Showing 2021Show all
3 papers · 1 filter
math.NA2021
Optimal design for kernel interpolation: applications to uncertainty quantification
Akil Narayan, Liang Yan, Tao Zhou
The paper is concerned with classic kernel interpolation methods, in addition to approximation methods that are augmented by gradient measurements. To apply kernel interpolation us…
math.NA2021
An acceleration strategy for randomize-then-optimize sampling via deep neural networks
Liang Yan, Tao Zhou
Randomize-then-optimize (RTO) is widely used for sampling from posterior distributions in Bayesian inverse problems. However, RTO may be computationally intensive for complexity pr…
math.NA2021★ 18 cited
Stein variational gradient descent with local approximations
Liang Yan, Tao Zhou
Bayesian computation plays an important role in modern machine learning and statistics to reason about uncertainty. A key computational challenge in Bayesian inference is to develo…