Multi Output Gaussian Process

Multi-output Gaussian processes (MOGPs) are statistical models designed to predict multiple correlated outputs simultaneously, leveraging shared information for improved accuracy and uncertainty quantification. Current research emphasizes addressing challenges like negative transfer (lack of correlation between outputs) and scalability to high-dimensional data, often employing techniques such as sparse covariance matrices, latent variable models, and variational inference for efficient computation. These advancements are improving the applicability of MOGPs across diverse fields, including reinforcement learning, drug discovery, and environmental modeling, by providing robust and uncertainty-aware predictions in complex systems.