Gaussian Process-based learning with new MCMC-based implementation of Wishart prior on correlation matrix
arXiv:2605.27093v1 Announce Type: cross Abstract: In probabilstic supervised learning of an input-output relationship - as a sample function of a Gaussian Process (GP) - priors are typically specified for the hyperparameters of the kernel that parametrises the covariance function of the GP, where the induced covariance matrix of the (resulting multivariate Normal) likelihood, governs the learning and prediction. When the sought function is highly multivariate, multiple lengthscale parameters must be learnt simultaneously, making inference difficult. We develop a ``self-assembled'' Wishart prio
This paper addresses a fundamental challenge in applying Gaussian Processes to highly multivariate problems, a growing area of interest as AI models become more complex and require robust uncertainty quantification.
Improved methods for managing uncertainty in complex AI systems are critical for the deployment of reliable and robust AI in sensitive applications and for advancing the state of the art in machine learning.
The proposed MCMC-based Wishart prior implementation potentially enables more accurate and stable inference in high-dimensional Gaussian Process models, expanding their applicability.
- · Machine Learning Researchers
- · AI developers
- · High-dimensional data analytics firms
- · Financial modeling sector
- · Companies relying on simpler, less robust probabilistic models
More accurate probabilistic predictions in complex systems through enhanced Gaussian Process models.
Accelerated development of AI applications requiring high-fidelity uncertainty estimation, such as in autonomous systems or drug discovery.
Increased adoption of Bayesian methods in scenarios where computational tractability was previously a bottleneck for advanced probabilistic reasoning.
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