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
ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs
Yogesh Verma, Markus Heinonen, Vikas Garg
Integrating Marketing Channels into Quantile Transformation and Bayesian Optimization of Ensemble Kernels for Sales Prediction with Gaussian Process Models
Shahin Mirshekari, Negin Hayeri Motedayen, Mohammad Ensaf
Informed Spectral Normalized Gaussian Processes for Trajectory Prediction
Christian Schlauch, Christian Wirth, Nadja Klein
A tutorial on learning from preferences and choices with Gaussian Processes
Alessio Benavoli, Dario Azzimonti
Neural network representation of quantum systems
Koji Hashimoto, Yuji Hirono, Jun Maeda, Jojiro Totsuka-Yoshinaka