{"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/improving-pareto-front-learning-via-multi","title":"Improving Pareto Front Learning via Multi-Sample Hypernetworks","arxiv_id":"2212.01130","date":"2022-12-02","proceeding":null,"authors":["Long P. Hoang","Dung D. Le","Tran Anh Tuan","Tran Ngoc Thang"],"abstract":"Pareto Front Learning (PFL) was recently introduced as an effective approach to obtain a mapping function from a given trade-off vector to a solution on the Pareto front, which solves the multi-objective optimization (MOO) problem. Due to the inherent trade-off between conflicting objectives, PFL offers a flexible approach in many scenarios in which the decision makers can not specify the preference of one Pareto solution over another, and must switch between them depending on the situation. However, existing PFL methods ignore the relationship between the solutions during the optimization process, which hinders the quality of the obtained front. To overcome this issue, we propose a novel PFL framework namely PHN-HVI, which employs a hypernetwork to generate multiple solutions from a set of diverse trade-off preferences and enhance the quality of the Pareto front by maximizing the Hypervolume indicator defined by these solutions. The experimental results on several MOO machine learning tasks show that the proposed framework significantly outperforms the baselines in producing the trade-off Pareto front.","url_abs":"https://arxiv.org/abs/2212.01130v7","url_pdf":"https://arxiv.org/pdf/2212.01130v7.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":"improving-pareto-front-learning-via-multi","repo_url":"https://github.com/longhoangphi225/MultiSample-Hypernetworks","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[{"method_slug":"hypernetwork","method_name":"HyperNetwork"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2212.01130","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.01130"}},"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/longhoangphi225/MultiSample-Hypernetworks","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran_honours":2,"unverified":8},"by_repo_kind":{"official":{"samples":10,"ran":2,"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":0,"samples":[{"code_sha256_prefix":"f76ba0f4cbb3d733","entry":"circle_points","repo":"longhoangphi225/MultiSample-Hypernetworks","repo_kind":"official","path":"Jura/utils.py","file_url":"https://github.com/longhoangphi225/MultiSample-Hypernetworks/blob/HEAD/Jura/utils.py","link_basis":"harvester_set","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f76ba0f4cbb3d733"}},{"code_sha256_prefix":"f6b944f50d3f15ae","entry":"count_parameters","repo":"longhoangphi225/MultiSample-Hypernetworks","repo_kind":"official","path":"Jura/utils.py","file_url":"https://github.com/longhoangphi225/MultiSample-Hypernetworks/blob/HEAD/Jura/utils.py","link_basis":"harvester_set","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f6b944f50d3f15ae"}},{"code_sha256_prefix":"42d7425cbbe80c4f","entry":"checkDomination","repo":"longhoangphi225/MultiSample-Hypernetworks","repo_kind":"official","path":"Jura/functions_evaluation.py","file_url":"https://github.com/longhoangphi225/MultiSample-Hypernetworks/blob/HEAD/Jura/functions_evaluation.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"42d7425cbbe80c4f"}},{"code_sha256_prefix":"323b62405a8df3d5","entry":"determine_mo_sol_in_exterior","repo":"longhoangphi225/MultiSample-Hypernetworks","repo_kind":"official","path":"Jura/functions_hv_grad_3d.py","file_url":"https://github.com/longhoangphi225/MultiSample-Hypernetworks/blob/HEAD/Jura/functions_hv_grad_3d.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"323b62405a8df3d5"}},{"code_sha256_prefix":"4c8ef15f99bfbd06","entry":"determine_mo_sol_in_interior","repo":"longhoangphi225/MultiSample-Hypernetworks","repo_kind":"official","path":"Jura/functions_hv_grad_3d.py","file_url":"https://github.com/longhoangphi225/MultiSample-Hypernetworks/blob/HEAD/Jura/functions_hv_grad_3d.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"4c8ef15f99bfbd06"}},{"code_sha256_prefix":"b14d93e8c7be509c","entry":"determine_mo_sol_on_ref_boundary","repo":"longhoangphi225/MultiSample-Hypernetworks","repo_kind":"official","path":"Jura/functions_hv_grad_3d.py","file_url":"https://github.com/longhoangphi225/MultiSample-Hypernetworks/blob/HEAD/Jura/functions_hv_grad_3d.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"b14d93e8c7be509c"}},{"code_sha256_prefix":"51349084c756d3a1","entry":"determine_non_dom_mo_sol","repo":"longhoangphi225/MultiSample-Hypernetworks","repo_kind":"official","path":"Jura/functions_evaluation.py","file_url":"https://github.com/longhoangphi225/MultiSample-Hypernetworks/blob/HEAD/Jura/functions_evaluation.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"51349084c756d3a1"}},{"code_sha256_prefix":"7a212c0b10ec2e95","entry":"fastNonDominatedSort","repo":"longhoangphi225/MultiSample-Hypernetworks","repo_kind":"official","path":"Jura/functions_evaluation.py","file_url":"https://github.com/longhoangphi225/MultiSample-Hypernetworks/blob/HEAD/Jura/functions_evaluation.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"7a212c0b10ec2e95"}},{"code_sha256_prefix":"398ef1efd0ea4048","entry":"get_device","repo":"longhoangphi225/MultiSample-Hypernetworks","repo_kind":"official","path":"Jura/utils.py","file_url":"https://github.com/longhoangphi225/MultiSample-Hypernetworks/blob/HEAD/Jura/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"398ef1efd0ea4048"}},{"code_sha256_prefix":"f745c0a67e824256","entry":"get_losses","repo":"longhoangphi225/MultiSample-Hypernetworks","repo_kind":"official","path":"Jura/trainer.py","file_url":"https://github.com/longhoangphi225/MultiSample-Hypernetworks/blob/HEAD/Jura/trainer.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f745c0a67e824256"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}