{"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/parametric-programming-approach-for-powerful","title":"Parametric Programming Approach for More Powerful and General Lasso Selective Inference","arxiv_id":"2004.09749","date":"2020-04-21","proceeding":null,"authors":["Vo Nguyen Le Duy","Ichiro Takeuchi"],"abstract":"Selective Inference (SI) has been actively studied in the past few years for conducting inference on the features of linear models that are adaptively selected by feature selection methods such as Lasso. The basic idea of SI is to make inference conditional on the selection event. Unfortunately, the main limitation of the original SI approach for Lasso is that the inference is conducted not only conditional on the selected features but also on their signs -- this leads to loss of power because of over-conditioning. Although this limitation can be circumvented by considering the union of such selection events for all possible combinations of signs, this is only feasible when the number of selected features is sufficiently small. To address this computational bottleneck, we propose a parametric programming-based method that can conduct SI without conditioning on signs even when we have thousands of active features. The main idea is to compute the continuum path of Lasso solutions in the direction of a test statistic, and identify the subset of the data space corresponding to the feature selection event by following the solution path. The proposed parametric programming-based method not only avoids the aforementioned computational bottleneck but also improves the performance and practicality of SI for Lasso in various respects. We conduct several experiments to demonstrate the effectiveness and efficiency of our proposed method.","url_abs":"https://arxiv.org/abs/2004.09749v3","url_pdf":"https://arxiv.org/pdf/2004.09749v3.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":"parametric-programming-approach-for-powerful","repo_url":"https://github.com/vonguyenleduy/parametric_lasso_selective_inference","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"parametric-programming-approach-for-powerful","repo_url":"https://github.com/takeuchi-lab/parametric-si","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"parametric-programming-approach-for-powerful","repo_url":"https://github.com/vonguyenleduy/parametric_generalized_lasso_selective_inference","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[{"method_slug":"feature-selection","method_name":"Feature Selection"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2004.09749","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.09749"}},"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/vonguyenleduy/parametric_generalized_lasso_selective_inference","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/vonguyenleduy/parametric_lasso_selective_inference","reach":{"status":"ok","spdx":"NOASSERTION"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/takeuchi-lab/parametric-si","reach":{"status":"unanswered"}}],"summary":{"ran_draft_wrong":3},"by_repo_kind":{"listed":{"samples":3,"ran":3,"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":3,"samples":[{"code_sha256_prefix":"207838b3677af3a5","entry":"compute_c_d","repo":"vonguyenleduy/parametric_generalized_lasso_selective_inference","repo_kind":"listed","path":"fused_lasso/ex1_uniform_pivot.py","file_url":"https://github.com/vonguyenleduy/parametric_generalized_lasso_selective_inference/blob/HEAD/fused_lasso/ex1_uniform_pivot.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"207838b3677af3a5"}},{"code_sha256_prefix":"026b837e5182e6a8","entry":"construct_P_q_G_h_A_b","repo":"vonguyenleduy/parametric_generalized_lasso_selective_inference","repo_kind":"listed","path":"fused_lasso/ex1_uniform_pivot.py","file_url":"https://github.com/vonguyenleduy/parametric_generalized_lasso_selective_inference/blob/HEAD/fused_lasso/ex1_uniform_pivot.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"026b837e5182e6a8"}},{"code_sha256_prefix":"f5b32676b4567443","entry":"construct_P_q_G_h_A_b","repo":"vonguyenleduy/parametric_generalized_lasso_selective_inference","repo_kind":"listed","path":"vanilla_lasso/ex1_uniform_pivot.py","file_url":"https://github.com/vonguyenleduy/parametric_generalized_lasso_selective_inference/blob/HEAD/vanilla_lasso/ex1_uniform_pivot.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"f5b32676b4567443"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}