{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:PDAC4TH3KCQY6LUI6BK4N2UGTX","short_pith_number":"pith:PDAC4TH3","canonical_record":{"source":{"id":"1409.1320","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-09-04T05:06:34Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"212a65aee7be3fb38e75fb74ccafd4e588a162e76cca8a5dc61f07dd071bf244","abstract_canon_sha256":"06060135cdd4616dd2ed81f4cd33e991000230309f0aca37f2337e333af33dc1"},"schema_version":"1.0"},"canonical_sha256":"78c02e4cfb50a18f2e88f055c6ea869df106a4ef2d4d6b79f6b324ffc4432e4e","source":{"kind":"arxiv","id":"1409.1320","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1409.1320","created_at":"2026-05-18T02:43:18Z"},{"alias_kind":"arxiv_version","alias_value":"1409.1320v2","created_at":"2026-05-18T02:43:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.1320","created_at":"2026-05-18T02:43:18Z"},{"alias_kind":"pith_short_12","alias_value":"PDAC4TH3KCQY","created_at":"2026-05-18T12:28:43Z"},{"alias_kind":"pith_short_16","alias_value":"PDAC4TH3KCQY6LUI","created_at":"2026-05-18T12:28:43Z"},{"alias_kind":"pith_short_8","alias_value":"PDAC4TH3","created_at":"2026-05-18T12:28:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:PDAC4TH3KCQY6LUI6BK4N2UGTX","target":"record","payload":{"canonical_record":{"source":{"id":"1409.1320","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-09-04T05:06:34Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"212a65aee7be3fb38e75fb74ccafd4e588a162e76cca8a5dc61f07dd071bf244","abstract_canon_sha256":"06060135cdd4616dd2ed81f4cd33e991000230309f0aca37f2337e333af33dc1"},"schema_version":"1.0"},"canonical_sha256":"78c02e4cfb50a18f2e88f055c6ea869df106a4ef2d4d6b79f6b324ffc4432e4e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:43:18.493971Z","signature_b64":"Pf0yLgPTDI8kS83YVpnhGghexmNbg+DcgvzqrfF/d/gXGz4DHBUofX46cd0EOwYCPzJ1wJKR7wK3I9ycq3+8AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"78c02e4cfb50a18f2e88f055c6ea869df106a4ef2d4d6b79f6b324ffc4432e4e","last_reissued_at":"2026-05-18T02:43:18.493467Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:43:18.493467Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1409.1320","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:43:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J4aM9XOygV7esHR3uacyMLOoc5UbltFCby+zIFMXDxPraDjt1DLymQ69bU+Ywh6ZLjCfysqT5zE6s5AfWlP+DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T20:06:23.523844Z"},"content_sha256":"5ab83057c054175eba4aa2bee1a7418457636144148051db6d9128f40eb7a34b","schema_version":"1.0","event_id":"sha256:5ab83057c054175eba4aa2bee1a7418457636144148051db6d9128f40eb7a34b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:PDAC4TH3KCQY6LUI6BK4N2UGTX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Marginal Structured SVM with Hidden Variables","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Alexander Ihler, Qiang Liu, Wei Ping","submitted_at":"2014-09-04T05:06:34Z","abstract_excerpt":"In this work, we propose the marginal structured SVM (MSSVM) for structured prediction with hidden variables. MSSVM properly accounts for the uncertainty of hidden variables, and can significantly outperform the previously proposed latent structured SVM (LSSVM; Yu & Joachims (2009)) and other state-of-art methods, especially when that uncertainty is large. Our method also results in a smoother objective function, making gradient-based optimization of MSSVMs converge significantly faster than for LSSVMs. We also show that our method consistently outperforms hidden conditional random fields (HCR"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.1320","kind":"arxiv","version":2},"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":"2026-05-18T02:43:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"n1uPc8SxQzlOnR3b5aq/nTHnWge6gajx4JbBzsSfUBCCjSjIU56fNTETXjhKpoR7CtYcbwt3mAlZyU8J9W+KAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T20:06:23.524363Z"},"content_sha256":"a22ccc382b9569c24774e44a59025ca6652a02cbb2944b56b2f0becd05e00170","schema_version":"1.0","event_id":"sha256:a22ccc382b9569c24774e44a59025ca6652a02cbb2944b56b2f0becd05e00170"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PDAC4TH3KCQY6LUI6BK4N2UGTX/bundle.json","state_url":"https://pith.science/pith/PDAC4TH3KCQY6LUI6BK4N2UGTX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PDAC4TH3KCQY6LUI6BK4N2UGTX/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":"2026-05-31T20:06:23Z","links":{"resolver":"https://pith.science/pith/PDAC4TH3KCQY6LUI6BK4N2UGTX","bundle":"https://pith.science/pith/PDAC4TH3KCQY6LUI6BK4N2UGTX/bundle.json","state":"https://pith.science/pith/PDAC4TH3KCQY6LUI6BK4N2UGTX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PDAC4TH3KCQY6LUI6BK4N2UGTX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:PDAC4TH3KCQY6LUI6BK4N2UGTX","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":"06060135cdd4616dd2ed81f4cd33e991000230309f0aca37f2337e333af33dc1","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-09-04T05:06:34Z","title_canon_sha256":"212a65aee7be3fb38e75fb74ccafd4e588a162e76cca8a5dc61f07dd071bf244"},"schema_version":"1.0","source":{"id":"1409.1320","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1409.1320","created_at":"2026-05-18T02:43:18Z"},{"alias_kind":"arxiv_version","alias_value":"1409.1320v2","created_at":"2026-05-18T02:43:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.1320","created_at":"2026-05-18T02:43:18Z"},{"alias_kind":"pith_short_12","alias_value":"PDAC4TH3KCQY","created_at":"2026-05-18T12:28:43Z"},{"alias_kind":"pith_short_16","alias_value":"PDAC4TH3KCQY6LUI","created_at":"2026-05-18T12:28:43Z"},{"alias_kind":"pith_short_8","alias_value":"PDAC4TH3","created_at":"2026-05-18T12:28:43Z"}],"graph_snapshots":[{"event_id":"sha256:a22ccc382b9569c24774e44a59025ca6652a02cbb2944b56b2f0becd05e00170","target":"graph","created_at":"2026-05-18T02:43:18Z","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":"In this work, we propose the marginal structured SVM (MSSVM) for structured prediction with hidden variables. MSSVM properly accounts for the uncertainty of hidden variables, and can significantly outperform the previously proposed latent structured SVM (LSSVM; Yu & Joachims (2009)) and other state-of-art methods, especially when that uncertainty is large. Our method also results in a smoother objective function, making gradient-based optimization of MSSVMs converge significantly faster than for LSSVMs. We also show that our method consistently outperforms hidden conditional random fields (HCR","authors_text":"Alexander Ihler, Qiang Liu, Wei Ping","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-09-04T05:06:34Z","title":"Marginal Structured SVM with Hidden Variables"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.1320","kind":"arxiv","version":2},"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":"sha256:5ab83057c054175eba4aa2bee1a7418457636144148051db6d9128f40eb7a34b","target":"record","created_at":"2026-05-18T02:43:18Z","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":"06060135cdd4616dd2ed81f4cd33e991000230309f0aca37f2337e333af33dc1","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-09-04T05:06:34Z","title_canon_sha256":"212a65aee7be3fb38e75fb74ccafd4e588a162e76cca8a5dc61f07dd071bf244"},"schema_version":"1.0","source":{"id":"1409.1320","kind":"arxiv","version":2}},"canonical_sha256":"78c02e4cfb50a18f2e88f055c6ea869df106a4ef2d4d6b79f6b324ffc4432e4e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"78c02e4cfb50a18f2e88f055c6ea869df106a4ef2d4d6b79f6b324ffc4432e4e","first_computed_at":"2026-05-18T02:43:18.493467Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:43:18.493467Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Pf0yLgPTDI8kS83YVpnhGghexmNbg+DcgvzqrfF/d/gXGz4DHBUofX46cd0EOwYCPzJ1wJKR7wK3I9ycq3+8AQ==","signature_status":"signed_v1","signed_at":"2026-05-18T02:43:18.493971Z","signed_message":"canonical_sha256_bytes"},"source_id":"1409.1320","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5ab83057c054175eba4aa2bee1a7418457636144148051db6d9128f40eb7a34b","sha256:a22ccc382b9569c24774e44a59025ca6652a02cbb2944b56b2f0becd05e00170"],"state_sha256":"5f7ffb5b0abc0535339b7e730ee11866d3eda70af5e3e656aca7699bad1e2217"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6A12iQ8fc0ChLbzk/a7uO04/Phs3yRsLmzZadPEPuYFAVtO9nOxSLB8pY4IsDPJ9eN4ylcoYo0BOwBYVgD+/AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T20:06:23.529134Z","bundle_sha256":"65660c1387f081678419d0a4e34a70e1bfcc22c672ab5a07de59fb274bf4a14b"}}