SIKA-GP: Accelerating Gaussian Process Inference with Sparse Inducing Kernel Approximations for Bayesian Deep Learning
arXiv:2605.26509v1 Announce Type: new Abstract: Gaussian processes (GPs) provide a principled Bayesian framework for uncertainty estimation, but their computational complexity severely limits scalability to large datasets. We propose SIKA-GP, which accelerates GP inference using sparse inducing kernel approximations based on a dyadic ordered template basis, incurring only ${O}(\log M)$ complexity dependence on the number of inducing points. Our approach constructs compact and expressive kernel representations from sparsely activated bases, enabling efficient tensorized GPU computation and seam
The continuous demand for more scalable and efficient AI models, especially in Bayesian Deep Learning, necessitates breakthroughs in computational bottlenecks for methods like Gaussian Processes.
Efficient Gaussian Processes with reduced computational complexity enable more robust uncertainty estimation and scalability in AI, crucial for reliable and deployable advanced machine learning systems.
The ability to deploy Gaussian Processes on larger datasets and with less computational overhead changes the practicality of Bayesian methods in real-world AI applications, potentially accelerating their adoption.
- · AI researchers and developers
- · Cloud AI providers
- · Industries requiring robust uncertainty quantification (e.g., finance, healthcar
- · GPU manufacturers
- · Developers of less efficient Bayesian inference methods
Acceleration of Gaussian Process inference with improved scalability and reduced computational cost.
Wider adoption of Bayesian Deep Learning in production systems due to enhanced performance and uncertainty quantification capabilities.
Increased reliability and trustworthiness of AI models across critical applications, potentially leading to new regulatory and ethical considerations for advanced AI deployment.
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Read at arXiv cs.LG