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Though fundamental and widely applicable, nonparametric conditional\ndensity estimators have received relatively little attention from statisticians\nand little or none from the machine learning community. None of that work has\nbeen applied to greater than bivariate data, presumably due to the\ncomputational difficulty of data-driven bandwidth selection. We describe the\ndouble kernel conditional density estimator and derive fast dual-tree-based\nalgorithms for bandwidth selection using a maximum likelihood criterion. These\ntechniques give speedups of up to 3.8 million in our experiments, and enable\nthe first applications to previously intractable large multivariate datasets,\nincluding a redshift prediction problem from the Sloan Digital Sky Survey.","url_abs":"http://arxiv.org/abs/1206.5278v1","url_pdf":"http://arxiv.org/pdf/1206.5278v1.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":"fast-nonparametric-conditional-density","repo_url":"https://github.com/tommyod/KDEpy","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"density-estimation","task_name":"Density Estimation"},{"task_slug":"prediction-intervals","task_name":"Prediction Intervals"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1206.5278","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1206.5278"}},"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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