arXiv:2606.02909v1 Announce Type: cross Abstract: Gradient observations can substantially improve Gaussian process (GP) surrogates, particularly in high-dimensional settings where function evaluations are expensive. However, exact inference with $n$ function values and $n$ full gradients in $d$ dimensions scales cubically in the joint state size, imposing an intractable $\mathcal{O}(n^3 d^3)$ computational bottleneck. We introduce TERA, a highly scalable derivative GP method based on target-specific exact gradient reduction. We prove that for stationary kernels, the gradient components orthogo
The continuous growth in demand for complex AI models in high-dimensional settings necessitates more efficient computational methods for gradient-based optimization and inference.
This development addresses a critical computational bottleneck in high-dimensional Gaussian processes, enabling more scalable and efficient AI models for various applications where gradient information is crucial.
The ability to perform exact inference with derivative Gaussian processes at a significantly reduced computational cost ($\mathcal{O}(n^3)$ instead of $\mathcal{O}(n^3 d^3)$) allows for the deployment of more sophisticated AI in previously intractable problem spaces.
- · AI researchers
- · Machine learning engineers
- · Industries using high-dimensional data
- · Developers of less efficient GP methods
- · Organizations constrained by current computational limits
More powerful and complex AI models can be trained and deployed for tasks requiring gradient information.
Accelerated research and development in areas like computational design, scientific simulations, and drug discovery due to improved AI capabilities.
Enhanced automation and decision-making systems in various sectors as AI becomes more capable of handling intricate, high-dimensional problems efficiently.
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Read at arXiv cs.LG