{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:7Y43VGYXSSXZEEPK2TP6LZN35B","short_pith_number":"pith:7Y43VGYX","schema_version":"1.0","canonical_sha256":"fe39ba9b1794af9211ead4dfe5e5bbe85910e63a751c3c4087f4331690388d7e","source":{"kind":"arxiv","id":"1906.08397","version":1},"attestation_state":"computed","paper":{"title":"Adversarial Self-Paced Learning for Mixture Models of Hawkes Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Dixin Luo, Hongteng Xu, Lawrence Carin","submitted_at":"2019-06-20T00:24:02Z","abstract_excerpt":"We propose a novel adversarial learning strategy for mixture models of Hawkes processes, leveraging data augmentation techniques of Hawkes process in the framework of self-paced learning. Instead of learning a mixture model directly from a set of event sequences drawn from different Hawkes processes, the proposed method learns the target model iteratively, which generates \"easy\" sequences and uses them in an adversarial and self-paced manner. In each iteration, we first generate a set of augmented sequences from original observed sequences. Based on the fact that an easy sample of the target m"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1906.08397","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-06-20T00:24:02Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"36056b19856117360b86968ed49a4eaef25334203638c64f0d99d8563da85616","abstract_canon_sha256":"c884b2c61a2d9e0dfc19e6b1a09c4ef519b09bc82fffc8a7348a325b7db5d53c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:52.601197Z","signature_b64":"vjTq+NreCGZgC49DssSlTjtC6g26Xh7ThED+PjypNccpdcpxE1UkxUQ+wxj95IKdrEy4Rnc/1n0qqGM6UQsoDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe39ba9b1794af9211ead4dfe5e5bbe85910e63a751c3c4087f4331690388d7e","last_reissued_at":"2026-05-17T23:42:52.600632Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:52.600632Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adversarial Self-Paced Learning for Mixture Models of Hawkes Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Dixin Luo, Hongteng Xu, Lawrence Carin","submitted_at":"2019-06-20T00:24:02Z","abstract_excerpt":"We propose a novel adversarial learning strategy for mixture models of Hawkes processes, leveraging data augmentation techniques of Hawkes process in the framework of self-paced learning. Instead of learning a mixture model directly from a set of event sequences drawn from different Hawkes processes, the proposed method learns the target model iteratively, which generates \"easy\" sequences and uses them in an adversarial and self-paced manner. In each iteration, we first generate a set of augmented sequences from original observed sequences. Based on the fact that an easy sample of the target m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.08397","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1906.08397","created_at":"2026-05-17T23:42:52.600719+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.08397v1","created_at":"2026-05-17T23:42:52.600719+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.08397","created_at":"2026-05-17T23:42:52.600719+00:00"},{"alias_kind":"pith_short_12","alias_value":"7Y43VGYXSSXZ","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"7Y43VGYXSSXZEEPK","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"7Y43VGYX","created_at":"2026-05-18T12:33:12.712433+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7Y43VGYXSSXZEEPK2TP6LZN35B","json":"https://pith.science/pith/7Y43VGYXSSXZEEPK2TP6LZN35B.json","graph_json":"https://pith.science/api/pith-number/7Y43VGYXSSXZEEPK2TP6LZN35B/graph.json","events_json":"https://pith.science/api/pith-number/7Y43VGYXSSXZEEPK2TP6LZN35B/events.json","paper":"https://pith.science/paper/7Y43VGYX"},"agent_actions":{"view_html":"https://pith.science/pith/7Y43VGYXSSXZEEPK2TP6LZN35B","download_json":"https://pith.science/pith/7Y43VGYXSSXZEEPK2TP6LZN35B.json","view_paper":"https://pith.science/paper/7Y43VGYX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.08397&json=true","fetch_graph":"https://pith.science/api/pith-number/7Y43VGYXSSXZEEPK2TP6LZN35B/graph.json","fetch_events":"https://pith.science/api/pith-number/7Y43VGYXSSXZEEPK2TP6LZN35B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7Y43VGYXSSXZEEPK2TP6LZN35B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7Y43VGYXSSXZEEPK2TP6LZN35B/action/storage_attestation","attest_author":"https://pith.science/pith/7Y43VGYXSSXZEEPK2TP6LZN35B/action/author_attestation","sign_citation":"https://pith.science/pith/7Y43VGYXSSXZEEPK2TP6LZN35B/action/citation_signature","submit_replication":"https://pith.science/pith/7Y43VGYXSSXZEEPK2TP6LZN35B/action/replication_record"}},"created_at":"2026-05-17T23:42:52.600719+00:00","updated_at":"2026-05-17T23:42:52.600719+00:00"}