Data to 14 Oct 2026

GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration

arXiv 1809.11165 ↗First posted 28 Sep 2018cs.LG stat.MLcited by 609

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Abstract (arXiv)

Despite advances in scalable models, the inference tools used for Gaussian processes (GPs) have yet to fully capitalize on developments in computing hardware. We present an efficient and general approach to GP inference based on Blackbox Matrix-Matrix multiplication (BBMM). BBMM inference uses a modified batched version of the conjugate gradients algorithm to derive all terms for training and inference in a single call. BBMM reduces the asymptotic complexity of exact GP inference from O(n3) to O(n2). Adapting this algorithm to scalable approximations and complex GP models simply requires a routine for efficient matrix-matrix multiplication with the kernel and its derivative. In addition, BBMM uses a specialized preconditioner to substantially speed up convergence. In experiments we show that BBMM effectively uses GPU hardware to dramatically accelerate both exact GP inference and scalable approximations. Additionally, we provide GPyTorch, a software platform for scalable GP inference via BBMM, built on PyTorch.

Key facts. Cited by 609 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Sep 2018.

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Citing papers per year: 2021: 87; 2022: 109; 2023: 103; 2024: 113; 2025: 110; 2026: 87.

609 papers.

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2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 608
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