{"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/private-synthetic-data-for-multitask-learning","title":"Private Synthetic Data for Multitask Learning and Marginal Queries","arxiv_id":"2209.07400","date":"2022-09-15","proceeding":null,"authors":["Giuseppe Vietri","Cedric Archambeau","Sergul Aydore","William Brown","Michael Kearns","Aaron Roth","Ankit Siva","Shuai Tang","Zhiwei Steven Wu"],"abstract":"We provide a differentially private algorithm for producing synthetic data simultaneously useful for multiple tasks: marginal queries and multitask machine learning (ML). A key innovation in our algorithm is the ability to directly handle numerical features, in contrast to a number of related prior approaches which require numerical features to be first converted into {high cardinality} categorical features via {a binning strategy}. Higher binning granularity is required for better accuracy, but this negatively impacts scalability. Eliminating the need for binning allows us to produce synthetic data preserving large numbers of statistical queries such as marginals on numerical features, and class conditional linear threshold queries. Preserving the latter means that the fraction of points of each class label above a particular half-space is roughly the same in both the real and synthetic data. This is the property that is needed to train a linear classifier in a multitask setting. Our algorithm also allows us to produce high quality synthetic data for mixed marginal queries, that combine both categorical and numerical features. Our method consistently runs 2-5x faster than the best comparable techniques, and provides significant accuracy improvements in both marginal queries and linear prediction tasks for mixed-type datasets.","url_abs":"https://arxiv.org/abs/2209.07400v1","url_pdf":"https://arxiv.org/pdf/2209.07400v1.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":"private-synthetic-data-for-multitask-learning","repo_url":"https://github.com/amazon-research/relaxed-adaptive-projection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"jax","reach":null},{"paper_slug":"private-synthetic-data-for-multitask-learning","repo_url":"https://github.com/amazon-science/relaxed-adaptive-projection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"jax","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2209.07400","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.07400"}},"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/amazon-science/relaxed-adaptive-projection","reach":{"status":"ok","spdx":"NOASSERTION"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/amazon-research/relaxed-adaptive-projection","reach":null}],"summary":{"ran":3,"ran_draft_wrong":1,"ran_honours":1,"unverified":7},"by_repo_kind":{"listed":{"samples":12,"ran":5,"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":12,"samples":[{"code_sha256_prefix":"9aa567d59f83e9d4","entry":"Domain","repo":"amazon-research/relaxed-adaptive-projection","repo_kind":"listed","path":"mechanisms/rap_pp.py","file_url":"https://github.com/amazon-research/relaxed-adaptive-projection/blob/HEAD/mechanisms/rap_pp.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"9aa567d59f83e9d4"}},{"code_sha256_prefix":"d27393ae9f746993","entry":"LogTime","repo":"amazon-research/relaxed-adaptive-projection","repo_kind":"listed","path":"mechanisms/rap_pp.py","file_url":"https://github.com/amazon-research/relaxed-adaptive-projection/blob/HEAD/mechanisms/rap_pp.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"d27393ae9f746993"}},{"code_sha256_prefix":"9cc1317ab7844603","entry":"ManageStats","repo":"amazon-research/relaxed-adaptive-projection","repo_kind":"listed","path":"mechanisms/rap_pp.py","file_url":"https://github.com/amazon-research/relaxed-adaptive-projection/blob/HEAD/mechanisms/rap_pp.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"9cc1317ab7844603"}},{"code_sha256_prefix":"be738d3900d465be","entry":"initialize_synthetic_dataset","repo":"amazon-research/relaxed-adaptive-projection","repo_kind":"listed","path":"mechanisms/rap_pp.py","file_url":"https://github.com/amazon-research/relaxed-adaptive-projection/blob/HEAD/mechanisms/rap_pp.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"be738d3900d465be"}},{"code_sha256_prefix":"bc4cc560bd7663fc","entry":"sparsemax","repo":"amazon-research/relaxed-adaptive-projection","repo_kind":"listed","path":"mechanisms/rap_pp.py","file_url":"https://github.com/amazon-research/relaxed-adaptive-projection/blob/HEAD/mechanisms/rap_pp.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"bc4cc560bd7663fc"}},{"code_sha256_prefix":"5c3d3e6d9ce4bdb4","entry":"BaseConfiguration","repo":"amazon-research/relaxed-adaptive-projection","repo_kind":"listed","path":"mechanisms/rap_pp.py","file_url":"https://github.com/amazon-research/relaxed-adaptive-projection/blob/HEAD/mechanisms/rap_pp.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"5c3d3e6d9ce4bdb4"}},{"code_sha256_prefix":"ba39aaf5a41b65f3","entry":"BaseMechanism","repo":"amazon-research/relaxed-adaptive-projection","repo_kind":"listed","path":"mechanisms/rap_pp.py","file_url":"https://github.com/amazon-research/relaxed-adaptive-projection/blob/HEAD/mechanisms/rap_pp.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"ba39aaf5a41b65f3"}},{"code_sha256_prefix":"4cfb7c38939bbc13","entry":"Dataset","repo":"amazon-research/relaxed-adaptive-projection","repo_kind":"listed","path":"mechanisms/rap_pp.py","file_url":"https://github.com/amazon-research/relaxed-adaptive-projection/blob/HEAD/mechanisms/rap_pp.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"4cfb7c38939bbc13"}},{"code_sha256_prefix":"762f5f3073e36182","entry":"RAPpp","repo":"amazon-research/relaxed-adaptive-projection","repo_kind":"listed","path":"mechanisms/rap_pp.py","file_url":"https://github.com/amazon-research/relaxed-adaptive-projection/blob/HEAD/mechanisms/rap_pp.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"762f5f3073e36182"}},{"code_sha256_prefix":"7a14b6f7000d4f34","entry":"RAPppConfiguration","repo":"amazon-research/relaxed-adaptive-projection","repo_kind":"listed","path":"mechanisms/rap_pp.py","file_url":"https://github.com/amazon-research/relaxed-adaptive-projection/blob/HEAD/mechanisms/rap_pp.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"7a14b6f7000d4f34"}},{"code_sha256_prefix":"677aecd970514308","entry":"print_bold","repo":"amazon-research/relaxed-adaptive-projection","repo_kind":"listed","path":"mechanisms/rap_pp.py","file_url":"https://github.com/amazon-research/relaxed-adaptive-projection/blob/HEAD/mechanisms/rap_pp.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"677aecd970514308"}},{"code_sha256_prefix":"461f51a17a3ac7d3","entry":"sparsemax_project","repo":"amazon-research/relaxed-adaptive-projection","repo_kind":"listed","path":"mechanisms/rap_pp.py","file_url":"https://github.com/amazon-research/relaxed-adaptive-projection/blob/HEAD/mechanisms/rap_pp.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"461f51a17a3ac7d3"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}