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
Environmental Sensor Placement with Convolutional Gaussian Neural Processes
Tom R. Andersson, Wessel P. Bruinsma, Stratis Markou, James Requeima, Alejandro Coca-Castro, Anna Vaughan, Anna-Louise Ellis, Matthew A. Lazzara, Dani Jones, J. Scott Hosking, Richard E. Turner
Challenges in Gaussian Processes for Non Intrusive Load Monitoring
Aadesh Desai, Gautam Vashishtha, Zeel B Patel, Nipun Batra
Entry Dependent Expert Selection in Distributed Gaussian Processes Using Multilabel Classification
Hamed Jalali, Gjergji Kasneci
Introduction and Exemplars of Uncertainty Decomposition
Shuo Chen
Neural Inference of Gaussian Processes for Time Series Data of Quasars
Egor Danilov, Aleksandra Ćiprijanović, Brian Nord
Scalable Bayesian Transformed Gaussian Processes
Xinran Zhu, Leo Huang, Cameron Ibrahim, Eric Hans Lee, David Bindel
Uncertainty Disentanglement with Non-stationary Heteroscedastic Gaussian Processes for Active Learning
Zeel B Patel, Nipun Batra, Kevin Murphy
Optimization on Manifolds via Graph Gaussian Processes
Hwanwoo Kim, Daniel Sanz-Alonso, Ruiyi Yang