Gaussian Process
Gaussian processes (GPs) are probabilistic models used for function approximation and uncertainty quantification, offering a powerful framework for various applications. Current research focuses on extending GPs' capabilities through novel architectures like deep GPs and hybrid models combining GPs with neural networks or other machine learning techniques, addressing scalability and computational efficiency challenges, particularly in high-dimensional or time-varying settings. These advancements are significantly impacting fields like robotics, control systems, and scientific modeling by providing robust, uncertainty-aware predictions and enabling more reliable decision-making in complex systems. The development of efficient algorithms and theoretical analyses further enhances the practical applicability and trustworthiness of GP-based methods.
Papers
Multi-Response Heteroscedastic Gaussian Process Models and Their Inference
Taehee Lee, Jun S. Liu
Heterogeneous Multi-Task Gaussian Cox Processes
Feng Zhou, Quyu Kong, Zhijie Deng, Fengxiang He, Peng Cui, Jun Zhu
Distributed multi-agent target search and tracking with Gaussian process and reinforcement learning
Jigang Kim, Dohyun Jang, H. Jin Kim
On Active Learning for Gaussian Process-based Global Sensitivity Analysis
Mohit Chauhan, Mariel Ojeda-Tuz, Ryan Catarelli, Kurtis Gurley, Dimitrios Tsapetis, Michael D. Shields
Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes
Talay M Cheema, Carl Edward Rasmussen