arXiv:2507.05064v4 Announce Type: replace-cross Abstract: Gaussian processes are flexible, probabilistic, non-parametric models widely used in machine learning and statistics. However, their scalability to large data sets is limited by computational constraints. To overcome these challenges, we propose Vecchia-inducing-points full-scale (VIF) approximations combining the strengths of global inducing points and local Vecchia approximations. Vecchia approximations excel in settings with low-dimensional inputs and moderately smooth covariance functions, while inducing point methods are better sui
The continuous drive to scale AI models and applications demands more efficient computational methods for machine learning, especially with increasing dataset sizes.
Improved approximations for Gaussian Processes can unlock their use in larger-scale applications across various AI fields, making sophisticated probabilistic modeling more accessible and efficient.
The computational bottleneck for Gaussian Processes is being addressed, enabling their application to larger datasets and potentially accelerating research and development in areas reliant on these models.
- · Machine Learning Researchers
- · Data Scientists
- · Cloud Computing Providers (more efficient resource use)
- · Industries using Bayesian Optimization
- · Developers of less efficient GP approximation methods
Gaussian Processes become a more viable option for large-scale, high-dimensional data analysis.
This could lead to advancements in fields like automation, drug discovery, and climate modeling where probabilistic uncertainty quantification is critical but currently computationally expensive.
More efficient tools for AI could subtly accelerate the development of agentic systems requiring robust uncertainty handling, impacting workflows in various sectors.
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Read at arXiv cs.LG