{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:CDPQCP6T7SUCVXYZ5QM2MX56H5","short_pith_number":"pith:CDPQCP6T","canonical_record":{"source":{"id":"1211.3966","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/3.0/","primary_cat":"cs.LG","submitted_at":"2012-11-16T17:48:42Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"93633dfd9a2d5c12ac17736e0f2d9120178f13fc67503fdeb7065018a9dff4ca","abstract_canon_sha256":"5b3a2147e35c476b37f326aa0da0a2f0815d8d15db4e16d832fec2dc5e2230b7"},"schema_version":"1.0"},"canonical_sha256":"10df013fd3fca82adf19ec19a65fbe3f7f53bd2bc2f6bcc7eca45073742caed2","source":{"kind":"arxiv","id":"1211.3966","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1211.3966","created_at":"2026-05-18T02:40:02Z"},{"alias_kind":"arxiv_version","alias_value":"1211.3966v3","created_at":"2026-05-18T02:40:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1211.3966","created_at":"2026-05-18T02:40:02Z"},{"alias_kind":"pith_short_12","alias_value":"CDPQCP6T7SUC","created_at":"2026-05-18T12:27:01Z"},{"alias_kind":"pith_short_16","alias_value":"CDPQCP6T7SUCVXYZ","created_at":"2026-05-18T12:27:01Z"},{"alias_kind":"pith_short_8","alias_value":"CDPQCP6T","created_at":"2026-05-18T12:27:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:CDPQCP6T7SUCVXYZ5QM2MX56H5","target":"record","payload":{"canonical_record":{"source":{"id":"1211.3966","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/3.0/","primary_cat":"cs.LG","submitted_at":"2012-11-16T17:48:42Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"93633dfd9a2d5c12ac17736e0f2d9120178f13fc67503fdeb7065018a9dff4ca","abstract_canon_sha256":"5b3a2147e35c476b37f326aa0da0a2f0815d8d15db4e16d832fec2dc5e2230b7"},"schema_version":"1.0"},"canonical_sha256":"10df013fd3fca82adf19ec19a65fbe3f7f53bd2bc2f6bcc7eca45073742caed2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:40:02.342096Z","signature_b64":"i3eVBs0bUHsW6rJ7VIEa7vlXpt2WsNt8LlnRwMtwfbLvjJ2EAGBfBQiDuBZ+8u/ImKwcGLuFVWMHu0M2xfdXBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"10df013fd3fca82adf19ec19a65fbe3f7f53bd2bc2f6bcc7eca45073742caed2","last_reissued_at":"2026-05-18T02:40:02.341644Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:40:02.341644Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1211.3966","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:40:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3n5EYwQisNa1kDI2m28tO+XJs+ukentcR6pBYuqpOA/clIzR/CcSPB/5ihgBbpP4ABcWlcYKN5j43jJqCLq/DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T23:27:20.965075Z"},"content_sha256":"9994601c6c9f2175d91a4b99545cd8404eeb7ec0c8a2b170799733048ddaaed3","schema_version":"1.0","event_id":"sha256:9994601c6c9f2175d91a4b99545cd8404eeb7ec0c8a2b170799733048ddaaed3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:CDPQCP6T7SUCVXYZ5QM2MX56H5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Lasso Screening Rules via Dual Polytope Projection","license":"http://creativecommons.org/licenses/by/3.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Jieping Ye, Jie Wang, Peter Wonka","submitted_at":"2012-11-16T17:48:42Z","abstract_excerpt":"Lasso is a widely used regression technique to find sparse representations. When the dimension of the feature space and the number of samples are extremely large, solving the Lasso problem remains challenging. To improve the efficiency of solving large-scale Lasso problems, El Ghaoui and his colleagues have proposed the SAFE rules which are able to quickly identify the inactive predictors, i.e., predictors that have $0$ components in the solution vector. Then, the inactive predictors or features can be removed from the optimization problem to reduce its scale. By transforming the standard Lass"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1211.3966","kind":"arxiv","version":3},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:40:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LiUNxbUikj09gasBmuAKRQ+8LcDdCaSXbF9DvpexpdlK+4nA95IMr3JDEvM4fpvu1NdnIVo5oY23F34h0ZOiBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T23:27:20.965482Z"},"content_sha256":"9524eef3b6b9af3c8474bd0660cbeebd9d707a2074235818014fcbd7b63e35b6","schema_version":"1.0","event_id":"sha256:9524eef3b6b9af3c8474bd0660cbeebd9d707a2074235818014fcbd7b63e35b6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CDPQCP6T7SUCVXYZ5QM2MX56H5/bundle.json","state_url":"https://pith.science/pith/CDPQCP6T7SUCVXYZ5QM2MX56H5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CDPQCP6T7SUCVXYZ5QM2MX56H5/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-31T23:27:20Z","links":{"resolver":"https://pith.science/pith/CDPQCP6T7SUCVXYZ5QM2MX56H5","bundle":"https://pith.science/pith/CDPQCP6T7SUCVXYZ5QM2MX56H5/bundle.json","state":"https://pith.science/pith/CDPQCP6T7SUCVXYZ5QM2MX56H5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CDPQCP6T7SUCVXYZ5QM2MX56H5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:CDPQCP6T7SUCVXYZ5QM2MX56H5","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"5b3a2147e35c476b37f326aa0da0a2f0815d8d15db4e16d832fec2dc5e2230b7","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/3.0/","primary_cat":"cs.LG","submitted_at":"2012-11-16T17:48:42Z","title_canon_sha256":"93633dfd9a2d5c12ac17736e0f2d9120178f13fc67503fdeb7065018a9dff4ca"},"schema_version":"1.0","source":{"id":"1211.3966","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1211.3966","created_at":"2026-05-18T02:40:02Z"},{"alias_kind":"arxiv_version","alias_value":"1211.3966v3","created_at":"2026-05-18T02:40:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1211.3966","created_at":"2026-05-18T02:40:02Z"},{"alias_kind":"pith_short_12","alias_value":"CDPQCP6T7SUC","created_at":"2026-05-18T12:27:01Z"},{"alias_kind":"pith_short_16","alias_value":"CDPQCP6T7SUCVXYZ","created_at":"2026-05-18T12:27:01Z"},{"alias_kind":"pith_short_8","alias_value":"CDPQCP6T","created_at":"2026-05-18T12:27:01Z"}],"graph_snapshots":[{"event_id":"sha256:9524eef3b6b9af3c8474bd0660cbeebd9d707a2074235818014fcbd7b63e35b6","target":"graph","created_at":"2026-05-18T02:40:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Lasso is a widely used regression technique to find sparse representations. When the dimension of the feature space and the number of samples are extremely large, solving the Lasso problem remains challenging. To improve the efficiency of solving large-scale Lasso problems, El Ghaoui and his colleagues have proposed the SAFE rules which are able to quickly identify the inactive predictors, i.e., predictors that have $0$ components in the solution vector. Then, the inactive predictors or features can be removed from the optimization problem to reduce its scale. By transforming the standard Lass","authors_text":"Jieping Ye, Jie Wang, Peter Wonka","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/3.0/","primary_cat":"cs.LG","submitted_at":"2012-11-16T17:48:42Z","title":"Lasso Screening Rules via Dual Polytope Projection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1211.3966","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9994601c6c9f2175d91a4b99545cd8404eeb7ec0c8a2b170799733048ddaaed3","target":"record","created_at":"2026-05-18T02:40:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"5b3a2147e35c476b37f326aa0da0a2f0815d8d15db4e16d832fec2dc5e2230b7","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/3.0/","primary_cat":"cs.LG","submitted_at":"2012-11-16T17:48:42Z","title_canon_sha256":"93633dfd9a2d5c12ac17736e0f2d9120178f13fc67503fdeb7065018a9dff4ca"},"schema_version":"1.0","source":{"id":"1211.3966","kind":"arxiv","version":3}},"canonical_sha256":"10df013fd3fca82adf19ec19a65fbe3f7f53bd2bc2f6bcc7eca45073742caed2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"10df013fd3fca82adf19ec19a65fbe3f7f53bd2bc2f6bcc7eca45073742caed2","first_computed_at":"2026-05-18T02:40:02.341644Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:40:02.341644Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"i3eVBs0bUHsW6rJ7VIEa7vlXpt2WsNt8LlnRwMtwfbLvjJ2EAGBfBQiDuBZ+8u/ImKwcGLuFVWMHu0M2xfdXBw==","signature_status":"signed_v1","signed_at":"2026-05-18T02:40:02.342096Z","signed_message":"canonical_sha256_bytes"},"source_id":"1211.3966","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9994601c6c9f2175d91a4b99545cd8404eeb7ec0c8a2b170799733048ddaaed3","sha256:9524eef3b6b9af3c8474bd0660cbeebd9d707a2074235818014fcbd7b63e35b6"],"state_sha256":"caecc19761f563369a40ccb46f7e86001bca06817f4ed15dcc955c76385bbd1e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OKCURUb+/RLUxB5cENn6kvfrlZCAuEwpFL39KBfXRwliTLIygm98IfZkk60byO8tMg8KhXKjLsbML8+M8uX8Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T23:27:20.968373Z","bundle_sha256":"f625f6f8ad7fd88efb6de68075ed3494b42a5bc9573a461c60e72a607932064c"}}