22 citations · 42 across the 10 of their papers we have counts for
4 papers · 1 filter
Multi-Fidelity Bayesian Optimization via Deep Neural Networks
Shibo Li, Wei Xing, Mike Kirby +1
Bayesian optimization (BO) is a popular framework to optimize black-box functions. In many applications, the objective function can be evaluated at multiple fidelities to enable a…
Multi-Fidelity High-Order Gaussian Processes for Physical Simulation
Zheng Wang, Wei Xing, Robert Kirby +1
The key task of physical simulation is to solve partial differential equations (PDEs) on discretized domains, which is known to be costly. In particular, high-fidelity solutions ar…
Physics Informed Deep Kernel Learning
Zheng Wang, Wei Xing, Robert Kirby +1
Deep kernel learning is a promising combination of deep neural networks and nonparametric function learning. However, as a data driven approach, the performance of deep kernel lear…
Scalable Variational Gaussian Process Regression Networks
Shibo Li, Wei Xing, Mike Kirby +1
Gaussian process regression networks (GPRN) are powerful Bayesian models for multi-output regression, but their inference is intractable. To address this issue, existing methods us…