{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:VUCZQVXOSP6SUYTNPYU6WIQIWA","short_pith_number":"pith:VUCZQVXO","schema_version":"1.0","canonical_sha256":"ad059856ee93fd2a626d7e29eb2208b023bed30d9df4183e02374da03412062e","source":{"kind":"arxiv","id":"2212.01130","version":7},"attestation_state":"computed","paper":{"title":"Improving Pareto Front Learning via Multi-Sample Hypernetworks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Dung D. Le, Long P. Hoang, Tran Anh Tuan, Tran Ngoc Thang","submitted_at":"2022-12-02T12:19:12Z","abstract_excerpt":"Pareto Front Learning (PFL) was recently introduced as an effective approach to obtain a mapping function from a given trade-off vector to a solution on the Pareto front, which solves the multi-objective optimization (MOO) problem. Due to the inherent trade-off between conflicting objectives, PFL offers a flexible approach in many scenarios in which the decision makers can not specify the preference of one Pareto solution over another, and must switch between them depending on the situation. However, existing PFL methods ignore the relationship between the solutions during the optimization pro"},"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":"2212.01130","kind":"arxiv","version":7},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-02T12:19:12Z","cross_cats_sorted":[],"title_canon_sha256":"0e528a9ee70a614756bc310e7ed645a362414cdec02cb44efadfdd9d4a960adb","abstract_canon_sha256":"9ea77350a0f3f3fa503af0bcf3d438eb2ae3b13d790e2ec019bfa927be41ee3f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:05:31.931741Z","signature_b64":"fDKT/jhLrbVXLF1sJTzP2xKYoJswYONRG+HsAK9nJl1zvOGRngnNp31cHDUpL70Ccx6brtRqjv/0AoeL04bwAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ad059856ee93fd2a626d7e29eb2208b023bed30d9df4183e02374da03412062e","last_reissued_at":"2026-07-05T06:05:31.931368Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:05:31.931368Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Pareto Front Learning via Multi-Sample Hypernetworks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Dung D. Le, Long P. Hoang, Tran Anh Tuan, Tran Ngoc Thang","submitted_at":"2022-12-02T12:19:12Z","abstract_excerpt":"Pareto Front Learning (PFL) was recently introduced as an effective approach to obtain a mapping function from a given trade-off vector to a solution on the Pareto front, which solves the multi-objective optimization (MOO) problem. Due to the inherent trade-off between conflicting objectives, PFL offers a flexible approach in many scenarios in which the decision makers can not specify the preference of one Pareto solution over another, and must switch between them depending on the situation. However, existing PFL methods ignore the relationship between the solutions during the optimization pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.01130","kind":"arxiv","version":7},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2212.01130/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2212.01130","created_at":"2026-07-05T06:05:31.931425+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.01130v7","created_at":"2026-07-05T06:05:31.931425+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.01130","created_at":"2026-07-05T06:05:31.931425+00:00"},{"alias_kind":"pith_short_12","alias_value":"VUCZQVXOSP6S","created_at":"2026-07-05T06:05:31.931425+00:00"},{"alias_kind":"pith_short_16","alias_value":"VUCZQVXOSP6SUYTN","created_at":"2026-07-05T06:05:31.931425+00:00"},{"alias_kind":"pith_short_8","alias_value":"VUCZQVXO","created_at":"2026-07-05T06:05:31.931425+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/VUCZQVXOSP6SUYTNPYU6WIQIWA","json":"https://pith.science/pith/VUCZQVXOSP6SUYTNPYU6WIQIWA.json","graph_json":"https://pith.science/api/pith-number/VUCZQVXOSP6SUYTNPYU6WIQIWA/graph.json","events_json":"https://pith.science/api/pith-number/VUCZQVXOSP6SUYTNPYU6WIQIWA/events.json","paper":"https://pith.science/paper/VUCZQVXO"},"agent_actions":{"view_html":"https://pith.science/pith/VUCZQVXOSP6SUYTNPYU6WIQIWA","download_json":"https://pith.science/pith/VUCZQVXOSP6SUYTNPYU6WIQIWA.json","view_paper":"https://pith.science/paper/VUCZQVXO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.01130&json=true","fetch_graph":"https://pith.science/api/pith-number/VUCZQVXOSP6SUYTNPYU6WIQIWA/graph.json","fetch_events":"https://pith.science/api/pith-number/VUCZQVXOSP6SUYTNPYU6WIQIWA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VUCZQVXOSP6SUYTNPYU6WIQIWA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VUCZQVXOSP6SUYTNPYU6WIQIWA/action/storage_attestation","attest_author":"https://pith.science/pith/VUCZQVXOSP6SUYTNPYU6WIQIWA/action/author_attestation","sign_citation":"https://pith.science/pith/VUCZQVXOSP6SUYTNPYU6WIQIWA/action/citation_signature","submit_replication":"https://pith.science/pith/VUCZQVXOSP6SUYTNPYU6WIQIWA/action/replication_record"}},"created_at":"2026-07-05T06:05:31.931425+00:00","updated_at":"2026-07-05T06:05:31.931425+00:00"}