{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:P6FJ3AI6VKB7ARWINJUTR7NEOL","short_pith_number":"pith:P6FJ3AI6","canonical_record":{"source":{"id":"1805.07844","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-20T23:49:25Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"56133515314a6b617d4b90518d992c26bb4098854dc02f968f90ae0d80028814","abstract_canon_sha256":"7f25fca1653195ce2e1e528be6e6dc46c0baeaa360ec6d20e0da6b12efd1cf34"},"schema_version":"1.0"},"canonical_sha256":"7f8a9d811eaa83f046c86a6938fda472fe29c255afc321cb2d46053b2edfc064","source":{"kind":"arxiv","id":"1805.07844","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.07844","created_at":"2026-05-18T00:15:32Z"},{"alias_kind":"arxiv_version","alias_value":"1805.07844v1","created_at":"2026-05-18T00:15:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.07844","created_at":"2026-05-18T00:15:32Z"},{"alias_kind":"pith_short_12","alias_value":"P6FJ3AI6VKB7","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"P6FJ3AI6VKB7ARWI","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"P6FJ3AI6","created_at":"2026-05-18T12:32:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:P6FJ3AI6VKB7ARWINJUTR7NEOL","target":"record","payload":{"canonical_record":{"source":{"id":"1805.07844","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-20T23:49:25Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"56133515314a6b617d4b90518d992c26bb4098854dc02f968f90ae0d80028814","abstract_canon_sha256":"7f25fca1653195ce2e1e528be6e6dc46c0baeaa360ec6d20e0da6b12efd1cf34"},"schema_version":"1.0"},"canonical_sha256":"7f8a9d811eaa83f046c86a6938fda472fe29c255afc321cb2d46053b2edfc064","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:32.372537Z","signature_b64":"5NubHdocPHesOzrstHCVYpDoRg+mEDp0LAJIDd6LeWduA2qbHa/JPVjtmWe5nsIBecmX30VxTu8LQV3OnAQZCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f8a9d811eaa83f046c86a6938fda472fe29c255afc321cb2d46053b2edfc064","last_reissued_at":"2026-05-18T00:15:32.371853Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:32.371853Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.07844","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:15:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OEJRYxDhKnUa43H9hlvZff8m6U8iHv2fj9LQ9R3NGFL20vva+6ffHSlqwjNOL+x/z4/3A55LAl8wXgJNVbTPAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T03:52:42.720279Z"},"content_sha256":"bccbe734d804554648803ed84afd9820c323c8f421baf524f34ed432f1737048","schema_version":"1.0","event_id":"sha256:bccbe734d804554648803ed84afd9820c323c8f421baf524f34ed432f1737048"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:P6FJ3AI6VKB7ARWINJUTR7NEOL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Projection-Free Algorithms in Statistical Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Chao Qu, Huan Xu, Yan Li","submitted_at":"2018-05-20T23:49:25Z","abstract_excerpt":"Frank-Wolfe algorithm (FW) and its variants have gained a surge of interests in machine learning community due to its projection-free property. Recently people have reduced the gradient evaluation complexity of FW algorithm to $\\log(\\frac{1}{\\epsilon})$ for the smooth and strongly convex objective. This complexity result is especially significant in learning problem, as the overwhelming data size makes a single evluation of gradient computational expensive. However, in high-dimensional statistical estimation problems, the objective is typically not strongly convex, and sometimes even non-conve"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.07844","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:15:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"E9z85PuAhPJ/jw9CbfwtSNbpvhT68g5NFBsiH6OZeDoUTXXotqCQX83SbBUbkn1xogh96+qJgDvhIIBSEeI6Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T03:52:42.720645Z"},"content_sha256":"7d03c4d5af2064efde7afb1d9a17f44e2941823640fe26f3c46ac98a727abec6","schema_version":"1.0","event_id":"sha256:7d03c4d5af2064efde7afb1d9a17f44e2941823640fe26f3c46ac98a727abec6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/P6FJ3AI6VKB7ARWINJUTR7NEOL/bundle.json","state_url":"https://pith.science/pith/P6FJ3AI6VKB7ARWINJUTR7NEOL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/P6FJ3AI6VKB7ARWINJUTR7NEOL/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":"2026-05-31T03:52:42Z","links":{"resolver":"https://pith.science/pith/P6FJ3AI6VKB7ARWINJUTR7NEOL","bundle":"https://pith.science/pith/P6FJ3AI6VKB7ARWINJUTR7NEOL/bundle.json","state":"https://pith.science/pith/P6FJ3AI6VKB7ARWINJUTR7NEOL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/P6FJ3AI6VKB7ARWINJUTR7NEOL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:P6FJ3AI6VKB7ARWINJUTR7NEOL","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":"7f25fca1653195ce2e1e528be6e6dc46c0baeaa360ec6d20e0da6b12efd1cf34","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-20T23:49:25Z","title_canon_sha256":"56133515314a6b617d4b90518d992c26bb4098854dc02f968f90ae0d80028814"},"schema_version":"1.0","source":{"id":"1805.07844","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.07844","created_at":"2026-05-18T00:15:32Z"},{"alias_kind":"arxiv_version","alias_value":"1805.07844v1","created_at":"2026-05-18T00:15:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.07844","created_at":"2026-05-18T00:15:32Z"},{"alias_kind":"pith_short_12","alias_value":"P6FJ3AI6VKB7","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"P6FJ3AI6VKB7ARWI","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"P6FJ3AI6","created_at":"2026-05-18T12:32:43Z"}],"graph_snapshots":[{"event_id":"sha256:7d03c4d5af2064efde7afb1d9a17f44e2941823640fe26f3c46ac98a727abec6","target":"graph","created_at":"2026-05-18T00:15:32Z","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":"Frank-Wolfe algorithm (FW) and its variants have gained a surge of interests in machine learning community due to its projection-free property. Recently people have reduced the gradient evaluation complexity of FW algorithm to $\\log(\\frac{1}{\\epsilon})$ for the smooth and strongly convex objective. This complexity result is especially significant in learning problem, as the overwhelming data size makes a single evluation of gradient computational expensive. However, in high-dimensional statistical estimation problems, the objective is typically not strongly convex, and sometimes even non-conve","authors_text":"Chao Qu, Huan Xu, Yan Li","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-20T23:49:25Z","title":"Projection-Free Algorithms in Statistical Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.07844","kind":"arxiv","version":1},"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:bccbe734d804554648803ed84afd9820c323c8f421baf524f34ed432f1737048","target":"record","created_at":"2026-05-18T00:15:32Z","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":"7f25fca1653195ce2e1e528be6e6dc46c0baeaa360ec6d20e0da6b12efd1cf34","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-20T23:49:25Z","title_canon_sha256":"56133515314a6b617d4b90518d992c26bb4098854dc02f968f90ae0d80028814"},"schema_version":"1.0","source":{"id":"1805.07844","kind":"arxiv","version":1}},"canonical_sha256":"7f8a9d811eaa83f046c86a6938fda472fe29c255afc321cb2d46053b2edfc064","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7f8a9d811eaa83f046c86a6938fda472fe29c255afc321cb2d46053b2edfc064","first_computed_at":"2026-05-18T00:15:32.371853Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:15:32.371853Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5NubHdocPHesOzrstHCVYpDoRg+mEDp0LAJIDd6LeWduA2qbHa/JPVjtmWe5nsIBecmX30VxTu8LQV3OnAQZCA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:15:32.372537Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.07844","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bccbe734d804554648803ed84afd9820c323c8f421baf524f34ed432f1737048","sha256:7d03c4d5af2064efde7afb1d9a17f44e2941823640fe26f3c46ac98a727abec6"],"state_sha256":"562be157125a65ca2a0fdcbf7ae2c3c55ece9ff4201a5614cd02f0b883ee76f5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZLfhKXL0kid9/CBUMR0PuyrgHwBr/wEnXMBXl9+n95/CqNa/UO9Uvex9sO4I6EYyG1I0FTxaj3Rst6t1tIPIDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T03:52:42.723138Z","bundle_sha256":"3e99a0b8c8a3fe2339bc15232a38eca74b46f52ff2a482fa422fafbdb54122dd"}}