{"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/determinantal-point-processes-for-machine","title":"Determinantal point processes for machine learning","arxiv_id":"1207.6083","date":"2012-07-25","proceeding":null,"authors":["Alex Kulesza","Ben Taskar"],"abstract":"Determinantal point processes (DPPs) are elegant probabilistic models of\nrepulsion that arise in quantum physics and random matrix theory. In contrast\nto traditional structured models like Markov random fields, which become\nintractable and hard to approximate in the presence of negative correlations,\nDPPs offer efficient and exact algorithms for sampling, marginalization,\nconditioning, and other inference tasks. We provide a gentle introduction to\nDPPs, focusing on the intuitions, algorithms, and extensions that are most\nrelevant to the machine learning community, and show how DPPs can be applied to\nreal-world applications like finding diverse sets of high-quality search\nresults, building informative summaries by selecting diverse sentences from\ndocuments, modeling non-overlapping human poses in images or video, and\nautomatically building timelines of important news stories.","url_abs":"http://arxiv.org/abs/1207.6083v4","url_pdf":"http://arxiv.org/pdf/1207.6083v4.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":"determinantal-point-processes-for-machine","repo_url":"https://github.com/UnofficialJuliaMirror/DeterminantalPointProcesses.jl-9d4a7304-c3b4-5347-99a8-9cc862165b3e","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"determinantal-point-processes-for-machine","repo_url":"https://github.com/UnofficialJuliaMirrorSnapshots/DeterminantalPointProcesses.jl-9d4a7304-c3b4-5347-99a8-9cc862165b3e","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"determinantal-point-processes-for-machine","repo_url":"https://github.com/alshedivat/DeterminantalPointProcesses.jl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"determinantal-point-processes-for-machine","repo_url":"https://github.com/theogf/DeterminantalPointProcesses.jl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"determinantal-point-processes-for-machine","repo_url":"https://github.com/guilgautier/DPPy","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"point-processes","task_name":"Point Processes"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1207.6083","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}