{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/provably-reliable-large-scale-sampling-from","title":"Provably Reliable Large-Scale Sampling from Gaussian Processes","arxiv_id":"2211.08036","date":"2022-11-15","proceeding":null,"authors":["Anthony Stephenson","Robert Allison","Edward Pyzer-Knapp"],"abstract":"When comparing approximate Gaussian process (GP) models, it can be helpful to be able to generate data from any GP. If we are interested in how approximate methods perform at scale, we may wish to generate very large synthetic datasets to evaluate them. Na\\\"{i}vely doing so would cost \\(\\mathcal{O}(n^3)\\) flops and \\(\\mathcal{O}(n^2)\\) memory to generate a size \\(n\\) sample. We demonstrate how to scale such data generation to large \\(n\\) whilst still providing guarantees that, with high probability, the sample is indistinguishable from a sample from the desired GP.","url_abs":"https://arxiv.org/abs/2211.08036v3","url_pdf":"https://arxiv.org/pdf/2211.08036v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"provably-reliable-large-scale-sampling-from","repo_url":"https://github.com/ant-stephenson/gpsampler","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2211.08036","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.08036"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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