{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:GALXRDEKFZUSTDRSCGDDFP6RMH","short_pith_number":"pith:GALXRDEK","canonical_record":{"source":{"id":"1608.00554","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2016-08-01T19:58:05Z","cross_cats_sorted":["math.PR","stat.ML"],"title_canon_sha256":"f02b0750f9990331c0b37f6affb4dadcbc54282badc31223c83124e40dbbbe2b","abstract_canon_sha256":"de1f159ccc7ab3e71293d6aa2c4af4f1066bbd255475de024bf7100c6b3b5b07"},"schema_version":"1.0"},"canonical_sha256":"3017788c8a2e69298e32118632bfd161e10b115787e8185d57f87c04c373380c","source":{"kind":"arxiv","id":"1608.00554","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.00554","created_at":"2026-05-18T00:45:54Z"},{"alias_kind":"arxiv_version","alias_value":"1608.00554v3","created_at":"2026-05-18T00:45:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.00554","created_at":"2026-05-18T00:45:54Z"},{"alias_kind":"pith_short_12","alias_value":"GALXRDEKFZUS","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_16","alias_value":"GALXRDEKFZUSTDRS","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_8","alias_value":"GALXRDEK","created_at":"2026-05-18T12:30:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:GALXRDEKFZUSTDRSCGDDFP6RMH","target":"record","payload":{"canonical_record":{"source":{"id":"1608.00554","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2016-08-01T19:58:05Z","cross_cats_sorted":["math.PR","stat.ML"],"title_canon_sha256":"f02b0750f9990331c0b37f6affb4dadcbc54282badc31223c83124e40dbbbe2b","abstract_canon_sha256":"de1f159ccc7ab3e71293d6aa2c4af4f1066bbd255475de024bf7100c6b3b5b07"},"schema_version":"1.0"},"canonical_sha256":"3017788c8a2e69298e32118632bfd161e10b115787e8185d57f87c04c373380c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:45:54.332534Z","signature_b64":"zyilbQVrkLk4CwjHVYV0bjO1OZsvuG8mNOaSBDzEyVTp2EloNE0HTx2grgk5lGYZajSXlVgp5nCZ/KGNM83wCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3017788c8a2e69298e32118632bfd161e10b115787e8185d57f87c04c373380c","last_reissued_at":"2026-05-18T00:45:54.331871Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:45:54.331871Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1608.00554","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":"2026-05-18T00:45:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zI+slVT3ZeYyStJb9SkAhnPaXbt/49Hg6GItMVhEFJVRYF4rbyWCxXTi//qCNly3POy/+ZN+nKHfeDke8J/xDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T17:39:28.138182Z"},"content_sha256":"6ab1ffb743da0689dd39e7c101edb8584fc1a62fd762a2665fb3dd4a7b13af7a","schema_version":"1.0","event_id":"sha256:6ab1ffb743da0689dd39e7c101edb8584fc1a62fd762a2665fb3dd4a7b13af7a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:GALXRDEKFZUSTDRSCGDDFP6RMH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"On the Complexity of Constrained Determinantal Point Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.PR","stat.ML"],"primary_cat":"cs.DS","authors_text":"Amit Deshpande, Damian Straszak, L. Elisa Celis, Nisheeth K. Vishnoi, Tarun Kathuria","submitted_at":"2016-08-01T19:58:05Z","abstract_excerpt":"Determinantal Point Processes (DPPs) are probabilistic models that arise in quantum physics and random matrix theory and have recently found numerous applications in computer science. DPPs define distributions over subsets of a given ground set, they exhibit interesting properties such as negative correlation, and, unlike other models, have efficient algorithms for sampling. When applied to kernel methods in machine learning, DPPs favor subsets of the given data with more diverse features. However, many real-world applications require efficient algorithms to sample from DPPs with additional co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.00554","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":"2026-05-18T00:45:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ma3DOpHwxvVCg8UPxKGfUN0ACRaiEwjsp6/ItIYluvFZmTkN0CMt9MAdSw481iLOdyDKG8Un729uIqE3GvqsCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T17:39:28.138743Z"},"content_sha256":"9a2e42a14f759ad6ea1c385f4bf9ea347bb85ea1addb1478856ddc51e8cbaf89","schema_version":"1.0","event_id":"sha256:9a2e42a14f759ad6ea1c385f4bf9ea347bb85ea1addb1478856ddc51e8cbaf89"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GALXRDEKFZUSTDRSCGDDFP6RMH/bundle.json","state_url":"https://pith.science/pith/GALXRDEKFZUSTDRSCGDDFP6RMH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GALXRDEKFZUSTDRSCGDDFP6RMH/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":"2026-05-26T17:39:28Z","links":{"resolver":"https://pith.science/pith/GALXRDEKFZUSTDRSCGDDFP6RMH","bundle":"https://pith.science/pith/GALXRDEKFZUSTDRSCGDDFP6RMH/bundle.json","state":"https://pith.science/pith/GALXRDEKFZUSTDRSCGDDFP6RMH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GALXRDEKFZUSTDRSCGDDFP6RMH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:GALXRDEKFZUSTDRSCGDDFP6RMH","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":"de1f159ccc7ab3e71293d6aa2c4af4f1066bbd255475de024bf7100c6b3b5b07","cross_cats_sorted":["math.PR","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2016-08-01T19:58:05Z","title_canon_sha256":"f02b0750f9990331c0b37f6affb4dadcbc54282badc31223c83124e40dbbbe2b"},"schema_version":"1.0","source":{"id":"1608.00554","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.00554","created_at":"2026-05-18T00:45:54Z"},{"alias_kind":"arxiv_version","alias_value":"1608.00554v3","created_at":"2026-05-18T00:45:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.00554","created_at":"2026-05-18T00:45:54Z"},{"alias_kind":"pith_short_12","alias_value":"GALXRDEKFZUS","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_16","alias_value":"GALXRDEKFZUSTDRS","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_8","alias_value":"GALXRDEK","created_at":"2026-05-18T12:30:15Z"}],"graph_snapshots":[{"event_id":"sha256:9a2e42a14f759ad6ea1c385f4bf9ea347bb85ea1addb1478856ddc51e8cbaf89","target":"graph","created_at":"2026-05-18T00:45:54Z","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) are probabilistic models that arise in quantum physics and random matrix theory and have recently found numerous applications in computer science. DPPs define distributions over subsets of a given ground set, they exhibit interesting properties such as negative correlation, and, unlike other models, have efficient algorithms for sampling. When applied to kernel methods in machine learning, DPPs favor subsets of the given data with more diverse features. However, many real-world applications require efficient algorithms to sample from DPPs with additional co","authors_text":"Amit Deshpande, Damian Straszak, L. Elisa Celis, Nisheeth K. Vishnoi, Tarun Kathuria","cross_cats":["math.PR","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2016-08-01T19:58:05Z","title":"On the Complexity of Constrained Determinantal Point Processes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.00554","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":"sha256:6ab1ffb743da0689dd39e7c101edb8584fc1a62fd762a2665fb3dd4a7b13af7a","target":"record","created_at":"2026-05-18T00:45:54Z","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":"de1f159ccc7ab3e71293d6aa2c4af4f1066bbd255475de024bf7100c6b3b5b07","cross_cats_sorted":["math.PR","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2016-08-01T19:58:05Z","title_canon_sha256":"f02b0750f9990331c0b37f6affb4dadcbc54282badc31223c83124e40dbbbe2b"},"schema_version":"1.0","source":{"id":"1608.00554","kind":"arxiv","version":3}},"canonical_sha256":"3017788c8a2e69298e32118632bfd161e10b115787e8185d57f87c04c373380c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3017788c8a2e69298e32118632bfd161e10b115787e8185d57f87c04c373380c","first_computed_at":"2026-05-18T00:45:54.331871Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:45:54.331871Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zyilbQVrkLk4CwjHVYV0bjO1OZsvuG8mNOaSBDzEyVTp2EloNE0HTx2grgk5lGYZajSXlVgp5nCZ/KGNM83wCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:45:54.332534Z","signed_message":"canonical_sha256_bytes"},"source_id":"1608.00554","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6ab1ffb743da0689dd39e7c101edb8584fc1a62fd762a2665fb3dd4a7b13af7a","sha256:9a2e42a14f759ad6ea1c385f4bf9ea347bb85ea1addb1478856ddc51e8cbaf89"],"state_sha256":"25defa9ab2750bf41d15e56358b2addeb94340a853c0f72d36e8b0e06d0e6d3b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6gKVL8AWFcHb/tEbq44l1lKKqIdvACW/7h5L8jhS7Juan7ktWBA7XnbzemdvY4BeDgY2xgIN6KhpVI5bi+tVDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T17:39:28.142449Z","bundle_sha256":"2a6ce0ffdfeaffdb474e41ae087b90c2409d0d62590beb744a8b93f761124c47"}}