{"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/ap-perf-incorporating-generic-performance","title":"AP-Perf: Incorporating Generic Performance Metrics in Differentiable Learning","arxiv_id":"1912.00965","date":"2019-12-02","proceeding":null,"authors":["Rizal Fathony","J. Zico Kolter"],"abstract":"We propose a method that enables practitioners to conveniently incorporate custom non-decomposable performance metrics into differentiable learning pipelines, notably those based upon neural network architectures. Our approach is based on the recently developed adversarial prediction framework, a distributionally robust approach that optimizes a metric in the worst case given the statistical summary of the empirical distribution. We formulate a marginal distribution technique to reduce the complexity of optimizing the adversarial prediction formulation over a vast range of non-decomposable metrics. We demonstrate how easy it is to write and incorporate complex custom metrics using our provided tool. Finally, we show the effectiveness of our approach various classification tasks on tabular datasets from the UCI repository and benchmark datasets, as well as image classification tasks. The code for our proposed method is available at https://github.com/rizalzaf/AdversarialPrediction.jl.","url_abs":"https://arxiv.org/abs/1912.00965v2","url_pdf":"https://arxiv.org/pdf/1912.00965v2.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":"ap-perf-incorporating-generic-performance","repo_url":"https://github.com/rizalzaf/AP-examples","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"ap-perf-incorporating-generic-performance","repo_url":"https://github.com/rizalzaf/AdversarialPrediction.jl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"ap-perf-incorporating-generic-performance","repo_url":"https://github.com/rizalzaf/ap_perf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"ap-perf-incorporating-generic-performance","repo_url":"https://github.com/rizalzaf/ap_perf_py","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1912.00965","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.00965"}},"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/rizalzaf/AdversarialPrediction.jl","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/rizalzaf/ap_perf_py","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/rizalzaf/AP-examples","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/rizalzaf/ap_perf","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":5},"by_repo_kind":{"listed":{"samples":5,"ran":0,"repositories":2}},"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":"b94c84b76136c545","entry":"load_data","repo":"rizalzaf/ap_perf_py","repo_kind":"listed","path":"simple.py","file_url":"https://github.com/rizalzaf/ap_perf_py/blob/HEAD/simple.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":"b94c84b76136c545"}},{"code_sha256_prefix":"264babbb39562cc0","entry":"marginal_projection","repo":"rizalzaf/ap_perf","repo_kind":"listed","path":"ap_perf/projection.py","file_url":"https://github.com/rizalzaf/ap_perf/blob/HEAD/ap_perf/projection.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":"264babbb39562cc0"}},{"code_sha256_prefix":"c678c401463fa71a","entry":"obj_rho","repo":"rizalzaf/ap_perf","repo_kind":"listed","path":"ap_perf/projection.py","file_url":"https://github.com/rizalzaf/ap_perf/blob/HEAD/ap_perf/projection.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":"c678c401463fa71a"}},{"code_sha256_prefix":"ccb14484708276f7","entry":"solve_p_given_abk","repo":"rizalzaf/ap_perf","repo_kind":"listed","path":"ap_perf/projection.py","file_url":"https://github.com/rizalzaf/ap_perf/blob/HEAD/ap_perf/projection.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":"ccb14484708276f7"}},{"code_sha256_prefix":"bee26ca4d596099b","entry":"sqrt","repo":"rizalzaf/ap_perf","repo_kind":"listed","path":"ap_perf/expression.py","file_url":"https://github.com/rizalzaf/ap_perf/blob/HEAD/ap_perf/expression.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":"bee26ca4d596099b"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}