Gaussian Process and Machine Learning

Dear Colleagues,

This special issue of journal of Mathematics, dedicated to exploring the intersection of Gaussian processes and machine learning. Gaussian processes offer a powerful framework nonparametric baysiean method for modeling complex data relationships and uncertainties, making them increasingly relevant in various areas of machine learning and statistical learning. In this special issue, we present a collection of articles that delve into the theoretical foundations, practical applications, and recent advancements in Gaussian process methodology in the area of machine learning. From regression and classification to optimization, time series analysis, solving stochastic differential equations, reinforcement learning, and their integration with deep learning, these articles showcase the versatility and potential of Gaussian processes in addressing a wide range of challenges in modern data analysis and decision making. We hope that this special issue will inspire further research and innovation in the field, ultimately advancing our understanding and utilization of Gaussian processes in machine learning. The scope of this special issue deals with the below area are but are not limited to them.

• Advances in Gaussian Process Regression for analysing real-world complex data
• Gaussian Process for Baysiean Optimization
• Gaussian Process for Time series analysis
• Gaussian process in Reinforcement Learning
• Gaussian Process in Deep Learning
• Physics-inform Neural network (PINNs) versus Gaussian process
• Gaussian process for solving stochastic diffrential equations

Dr. Omid Chatrabgoun
Dr. Alireza Daneshkhah
Guest Editors

Manuscript Submission Information

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• Advances in Gaussian Process Regression for analysing real-world complex data
• Gaussian Process for Baysiean Optimization
• Gaussian Process for Time series analysis
• Gaussian process in Reinforcement Learning
• Gaussian Process in Deep Learning
• Physics-inform Neural network (PINNs) versus Gaussian process
• Gaussian process for solving stochastic diffrential equations