{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:C3RNZDVIQTWXINOM63ZX2RBQVH","short_pith_number":"pith:C3RNZDVI","schema_version":"1.0","canonical_sha256":"16e2dc8ea884ed7435ccf6f37d4430a9d64c68ffd418f8720761dae160c4cfbb","source":{"kind":"arxiv","id":"1705.07256","version":1},"attestation_state":"computed","paper":{"title":"Learning Feature Nonlinearities with Non-Convex Regularized Binned Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT","math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jiasi Chen, Mehrdad Mahdavi, Samet Oymak","submitted_at":"2017-05-20T03:46:32Z","abstract_excerpt":"For various applications, the relations between the dependent and independent variables are highly nonlinear. Consequently, for large scale complex problems, neural networks and regression trees are commonly preferred over linear models such as Lasso. This work proposes learning the feature nonlinearities by binning feature values and finding the best fit in each quantile using non-convex regularized linear regression. The algorithm first captures the dependence between neighboring quantiles by enforcing smoothness via piecewise-constant/linear approximation and then selects a sparse subset of"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1705.07256","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-05-20T03:46:32Z","cross_cats_sorted":["cs.IT","math.IT","math.OC","stat.ML"],"title_canon_sha256":"8624f7f7d3dd9cc70bba9251f72484a35d3837f7a258991f0a96cd038dd7df3d","abstract_canon_sha256":"2febbea0af2097b51b8b1e377987197a00b6ecdf6dbb8e835a9a4c37a48d319e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:44:06.200888Z","signature_b64":"gLIo0udD6lIg8MgiOYf82n4N8gRxTPViRRY19yiKeBJbeXIPeXAFp03Bf9wm4DsNXxNWiN/K2jjMLoK4T8yODw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"16e2dc8ea884ed7435ccf6f37d4430a9d64c68ffd418f8720761dae160c4cfbb","last_reissued_at":"2026-05-18T00:44:06.200129Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:44:06.200129Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Feature Nonlinearities with Non-Convex Regularized Binned Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT","math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jiasi Chen, Mehrdad Mahdavi, Samet Oymak","submitted_at":"2017-05-20T03:46:32Z","abstract_excerpt":"For various applications, the relations between the dependent and independent variables are highly nonlinear. Consequently, for large scale complex problems, neural networks and regression trees are commonly preferred over linear models such as Lasso. This work proposes learning the feature nonlinearities by binning feature values and finding the best fit in each quantile using non-convex regularized linear regression. The algorithm first captures the dependence between neighboring quantiles by enforcing smoothness via piecewise-constant/linear approximation and then selects a sparse subset of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.07256","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1705.07256","created_at":"2026-05-18T00:44:06.200245+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.07256v1","created_at":"2026-05-18T00:44:06.200245+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.07256","created_at":"2026-05-18T00:44:06.200245+00:00"},{"alias_kind":"pith_short_12","alias_value":"C3RNZDVIQTWX","created_at":"2026-05-18T12:31:08.081275+00:00"},{"alias_kind":"pith_short_16","alias_value":"C3RNZDVIQTWXINOM","created_at":"2026-05-18T12:31:08.081275+00:00"},{"alias_kind":"pith_short_8","alias_value":"C3RNZDVI","created_at":"2026-05-18T12:31:08.081275+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/C3RNZDVIQTWXINOM63ZX2RBQVH","json":"https://pith.science/pith/C3RNZDVIQTWXINOM63ZX2RBQVH.json","graph_json":"https://pith.science/api/pith-number/C3RNZDVIQTWXINOM63ZX2RBQVH/graph.json","events_json":"https://pith.science/api/pith-number/C3RNZDVIQTWXINOM63ZX2RBQVH/events.json","paper":"https://pith.science/paper/C3RNZDVI"},"agent_actions":{"view_html":"https://pith.science/pith/C3RNZDVIQTWXINOM63ZX2RBQVH","download_json":"https://pith.science/pith/C3RNZDVIQTWXINOM63ZX2RBQVH.json","view_paper":"https://pith.science/paper/C3RNZDVI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.07256&json=true","fetch_graph":"https://pith.science/api/pith-number/C3RNZDVIQTWXINOM63ZX2RBQVH/graph.json","fetch_events":"https://pith.science/api/pith-number/C3RNZDVIQTWXINOM63ZX2RBQVH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C3RNZDVIQTWXINOM63ZX2RBQVH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C3RNZDVIQTWXINOM63ZX2RBQVH/action/storage_attestation","attest_author":"https://pith.science/pith/C3RNZDVIQTWXINOM63ZX2RBQVH/action/author_attestation","sign_citation":"https://pith.science/pith/C3RNZDVIQTWXINOM63ZX2RBQVH/action/citation_signature","submit_replication":"https://pith.science/pith/C3RNZDVIQTWXINOM63ZX2RBQVH/action/replication_record"}},"created_at":"2026-05-18T00:44:06.200245+00:00","updated_at":"2026-05-18T00:44:06.200245+00:00"}