4 papers
Robust Joint Modeling for Data with Continuous and Binary Responses
Yu Wang, Ran Jin, Lulu Kang
In many supervised learning applications, the response consists of both continuous and binary outcomes. Studies have shown that jointly modeling such mixed-type responses can subst…
Active Learning for Manifold Gaussian Process Regression
Yuanxing Cheng, Lulu Kang, Yiwei Wang +1
This paper introduces an active learning framework for manifold Gaussian Process (GP) regression, combining manifold learning with strategic data selection to improve accuracy in h…
Accelerating Particle-based Energetic Variational Inference
Xuelian Bao, Lulu Kang, Chun Liu +1
In this work, we propose a new particle-based variational inference (ParVI) method for accelerating the Energetic Variational Inference with Implicit scheme (EVI-Im) introduced in…
Energetic Variational Gaussian Process Regression for Computer Experiments
Lulu Kang, Yuanxing Cheng, Yiwei Wang +1
The Gaussian process (GP) regression model is a widely employed surrogate modeling technique for computer experiments, offering precise predictions and statistical inference for th…