{"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/fair-and-diverse-dpp-based-data-summarization","title":"Fair and Diverse DPP-based Data Summarization","arxiv_id":"1802.04023","date":"2018-02-12","proceeding":"ICML 2018 7","authors":["L. Elisa Celis","Vijay Keswani","Damian Straszak","Amit Deshpande","Tarun Kathuria","Nisheeth K. Vishnoi"],"abstract":"Sampling methods that choose a subset of the data proportional to its\ndiversity in the feature space are popular for data summarization. However,\nrecent studies have noted the occurrence of bias (under- or over-representation\nof a certain gender or race) in such data summarization methods. In this paper\nwe initiate a study of the problem of outputting a diverse and fair summary of\na given dataset. We work with a well-studied determinantal measure of diversity\nand corresponding distributions (DPPs) and present a framework that allows us\nto incorporate a general class of fairness constraints into such distributions.\nComing up with efficient algorithms to sample from these constrained\ndeterminantal distributions, however, suffers from a complexity barrier and we\npresent a fast sampler that is provably good when the input vectors satisfy a\nnatural property. Our experimental results on a real-world and an image dataset\nshow that the diversity of the samples produced by adding fairness constraints\nis not too far from the unconstrained case, and we also provide a theoretical\nexplanation of it.","url_abs":"http://arxiv.org/abs/1802.04023v1","url_pdf":"http://arxiv.org/pdf/1802.04023v1.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":"fair-and-diverse-dpp-based-data-summarization","repo_url":"https://github.com/DamianStraszak/FairDiverseDPPSampling","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"data-summarization","task_name":"Data Summarization"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"fairness","task_name":"Fairness"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.04023","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.04023"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/DamianStraszak/FairDiverseDPPSampling","reach":null}],"summary":{"unverified":2},"by_repo_kind":{"listed":{"samples":2,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":2,"samples":[{"code_sha256_prefix":"fd3a28f51316de85","entry":"add_sample","repo":"DamianStraszak/FairDiverseDPPSampling","repo_kind":"listed","path":"run_adult_experiment.py","file_url":"https://github.com/DamianStraszak/FairDiverseDPPSampling/blob/HEAD/run_adult_experiment.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"mcp_get_code":{"code_sha256":"fd3a28f51316de85"}},{"code_sha256_prefix":"54171d7de7807a09","entry":"data_sample","repo":"DamianStraszak/FairDiverseDPPSampling","repo_kind":"listed","path":"run_image_experiment.py","file_url":"https://github.com/DamianStraszak/FairDiverseDPPSampling/blob/HEAD/run_image_experiment.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"mcp_get_code":{"code_sha256":"54171d7de7807a09"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}