10 citations · 10 across the 3 of their papers we have counts for
4 papers
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…
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…
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…
An adaptive surrogate modeling based on deep neural networks for large-scale Bayesian inverse problems
Liang Yan, Tao Zhou
In Bayesian inverse problems, surrogate models are often constructed to speed up the computational procedure, as the parameter-to-data map can be very expensive to evaluate. Howeve…