{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:RUTKHBI65RG4LHAWRZOFDCHRUT","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":"62255b784c2c2b5c4e2e2f0383b2cb7abd8872524f2edf7c123d62b3858209d6","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-11-06T09:14:02Z","title_canon_sha256":"bdd76fbac80166f4d7b3877e578bf16b13c15d165095b043f8785a5f7c896297"},"schema_version":"1.0","source":{"id":"1411.1537","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1411.1537","created_at":"2026-05-18T02:38:26Z"},{"alias_kind":"arxiv_version","alias_value":"1411.1537v2","created_at":"2026-05-18T02:38:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1411.1537","created_at":"2026-05-18T02:38:26Z"},{"alias_kind":"pith_short_12","alias_value":"RUTKHBI65RG4","created_at":"2026-05-18T12:28:46Z"},{"alias_kind":"pith_short_16","alias_value":"RUTKHBI65RG4LHAW","created_at":"2026-05-18T12:28:46Z"},{"alias_kind":"pith_short_8","alias_value":"RUTKHBI6","created_at":"2026-05-18T12:28:46Z"}],"graph_snapshots":[{"event_id":"sha256:5b606849829d32cdd5d9c37dd903450a4a709e450bda64656d8aad639a6dc745","target":"graph","created_at":"2026-05-18T02:38:26Z","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":"Determinantal point processes (DPPs) offer a powerful approach to modeling diversity in many applications where the goal is to select a diverse subset. We study the problem of learning the parameters (the kernel matrix) of a DPP from labeled training data. We make two contributions. First, we show how to reparameterize a DPP's kernel matrix with multiple kernel functions, thus enhancing modeling flexibility. Second, we propose a novel parameter estimation technique based on the principle of large margin separation. In contrast to the state-of-the-art method of maximum likelihood estimation, ou","authors_text":"Boqing Gong, Fei Sha, Kristen Grauman, Wei-Lun Chao","cross_cats":["cs.CV","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-11-06T09:14:02Z","title":"Large-Margin Determinantal Point Processes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1411.1537","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":"sha256:1a92ba5f09f8b08f939ee9f566eed45adc0a6ba4e337809e105453a446fbe5ee","target":"record","created_at":"2026-05-18T02:38:26Z","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":"62255b784c2c2b5c4e2e2f0383b2cb7abd8872524f2edf7c123d62b3858209d6","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-11-06T09:14:02Z","title_canon_sha256":"bdd76fbac80166f4d7b3877e578bf16b13c15d165095b043f8785a5f7c896297"},"schema_version":"1.0","source":{"id":"1411.1537","kind":"arxiv","version":2}},"canonical_sha256":"8d26a3851eec4dc59c168e5c5188f1a4e3eb5b09420521c130ec8018c9985d53","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8d26a3851eec4dc59c168e5c5188f1a4e3eb5b09420521c130ec8018c9985d53","first_computed_at":"2026-05-18T02:38:26.078412Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:38:26.078412Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jfBmyL9WuZOmh+sVftJ8GAh4NdLB9eQhCbeE86Nfk5dBRbcDJvlvxCBtbsnUEGMEq5vLTJVUAt6jebm4YnHOCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T02:38:26.079052Z","signed_message":"canonical_sha256_bytes"},"source_id":"1411.1537","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1a92ba5f09f8b08f939ee9f566eed45adc0a6ba4e337809e105453a446fbe5ee","sha256:5b606849829d32cdd5d9c37dd903450a4a709e450bda64656d8aad639a6dc745"],"state_sha256":"cf1fc7dc87a37da18e371d9ed3051d8bee9e511cfbcb6fbdca9fc01fd85b4b34"}