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In this work, we estimate log determinants under the\nframework of maximum entropy, given information in the form of moment\nconstraints from stochastic trace estimation. The estimates demonstrate a\nsignificant improvement on state-of-the-art alternative methods, as shown on a\nwide variety of UFL sparse matrices. By taking the example of a general Markov\nrandom field, we also demonstrate how this approach can significantly\naccelerate inference in large-scale learning methods involving the log\ndeterminant.","url_abs":"http://arxiv.org/abs/1704.07223v1","url_pdf":"http://arxiv.org/pdf/1704.07223v1.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":"entropic-trace-estimates-for-log-determinants","repo_url":"https://github.com/11hifish/OptSketchTraceEst","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"},{"task_slug":"point-processes","task_name":"Point Processes"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1704.07223","atlas_url":"https://app.syntology.ai/?focus=1704.07223","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1704.07223"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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