{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:TRJGNWJWZEWS7HN3YWRRELSZ3P","short_pith_number":"pith:TRJGNWJW","canonical_record":{"source":{"id":"2110.03173","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-07T03:56:19Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4de45b7b9518225369dc4abfc6f7fb3aaafcd92dbc86754a7242156764cbfc00","abstract_canon_sha256":"3a3ba077e0d2d9290f61317ec0209a19f2ccb11d35b36b149af79776d113f187"},"schema_version":"1.0"},"canonical_sha256":"9c5266d936c92d2f9dbbc5a3122e59dbd2b1df01bb21c4af79e2853f155a537d","source":{"kind":"arxiv","id":"2110.03173","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.03173","created_at":"2026-07-05T08:53:59Z"},{"alias_kind":"arxiv_version","alias_value":"2110.03173v4","created_at":"2026-07-05T08:53:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.03173","created_at":"2026-07-05T08:53:59Z"},{"alias_kind":"pith_short_12","alias_value":"TRJGNWJWZEWS","created_at":"2026-07-05T08:53:59Z"},{"alias_kind":"pith_short_16","alias_value":"TRJGNWJWZEWS7HN3","created_at":"2026-07-05T08:53:59Z"},{"alias_kind":"pith_short_8","alias_value":"TRJGNWJW","created_at":"2026-07-05T08:53:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:TRJGNWJWZEWS7HN3YWRRELSZ3P","target":"record","payload":{"canonical_record":{"source":{"id":"2110.03173","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-07T03:56:19Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4de45b7b9518225369dc4abfc6f7fb3aaafcd92dbc86754a7242156764cbfc00","abstract_canon_sha256":"3a3ba077e0d2d9290f61317ec0209a19f2ccb11d35b36b149af79776d113f187"},"schema_version":"1.0"},"canonical_sha256":"9c5266d936c92d2f9dbbc5a3122e59dbd2b1df01bb21c4af79e2853f155a537d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:53:59.838918Z","signature_b64":"x1Ic1FgPPXVLeap9sTV9qJv/66jOsHqjQ4yxczeznsKYqSUBSuQyaJ2Z2MX2nbcILMQt78o6gTQQgnaPT1JaCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9c5266d936c92d2f9dbbc5a3122e59dbd2b1df01bb21c4af79e2853f155a537d","last_reissued_at":"2026-07-05T08:53:59.838477Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:53:59.838477Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2110.03173","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:53:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DC4onbK4inXu7EpkcLIdNNIttC+ZyPdRD66SZh+qzf6lSV6/F0AZ77ure1KJUfPvlXHWzo6zMKSjhzkO5s5SDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T00:38:30.845925Z"},"content_sha256":"2d285dad3651f0519a3ce40262909746dccbc15b7e12e9f097e982a67ee2bd72","schema_version":"1.0","event_id":"sha256:2d285dad3651f0519a3ce40262909746dccbc15b7e12e9f097e982a67ee2bd72"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:TRJGNWJWZEWS7HN3YWRRELSZ3P","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-objective Optimization by Learning Space Partitions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Kevin Yang, Linnan Wang, Tian Guo, Tianjun Zhang, Yiyang Zhao, Yuandong Tian","submitted_at":"2021-10-07T03:56:19Z","abstract_excerpt":"In contrast to single-objective optimization (SOO), multi-objective optimization (MOO) requires an optimizer to find the Pareto frontier, a subset of feasible solutions that are not dominated by other feasible solutions. In this paper, we propose LaMOO, a novel multi-objective optimizer that learns a model from observed samples to partition the search space and then focus on promising regions that are likely to contain a subset of the Pareto frontier. The partitioning is based on the dominance number, which measures \"how close\" a data point is to the Pareto frontier among existing samples. To "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.03173","kind":"arxiv","version":4},"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/2110.03173/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:53:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XR593s8C9ARfu3f0Wy4fzmd/GHV2ulPMLoHW7RLfBgjICSIB/G66bClK0gBLMLvrX3QgZ48v4HoJVYNp4A9zDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T00:38:30.846789Z"},"content_sha256":"46288604c74203f5ee3d2c00c3a105289a10f682fc32787bf8ae2b34b24e6713","schema_version":"1.0","event_id":"sha256:46288604c74203f5ee3d2c00c3a105289a10f682fc32787bf8ae2b34b24e6713"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TRJGNWJWZEWS7HN3YWRRELSZ3P/bundle.json","state_url":"https://pith.science/pith/TRJGNWJWZEWS7HN3YWRRELSZ3P/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TRJGNWJWZEWS7HN3YWRRELSZ3P/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":"2026-08-15T00:38:30Z","links":{"resolver":"https://pith.science/pith/TRJGNWJWZEWS7HN3YWRRELSZ3P","bundle":"https://pith.science/pith/TRJGNWJWZEWS7HN3YWRRELSZ3P/bundle.json","state":"https://pith.science/pith/TRJGNWJWZEWS7HN3YWRRELSZ3P/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TRJGNWJWZEWS7HN3YWRRELSZ3P/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:TRJGNWJWZEWS7HN3YWRRELSZ3P","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":"3a3ba077e0d2d9290f61317ec0209a19f2ccb11d35b36b149af79776d113f187","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-07T03:56:19Z","title_canon_sha256":"4de45b7b9518225369dc4abfc6f7fb3aaafcd92dbc86754a7242156764cbfc00"},"schema_version":"1.0","source":{"id":"2110.03173","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.03173","created_at":"2026-07-05T08:53:59Z"},{"alias_kind":"arxiv_version","alias_value":"2110.03173v4","created_at":"2026-07-05T08:53:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.03173","created_at":"2026-07-05T08:53:59Z"},{"alias_kind":"pith_short_12","alias_value":"TRJGNWJWZEWS","created_at":"2026-07-05T08:53:59Z"},{"alias_kind":"pith_short_16","alias_value":"TRJGNWJWZEWS7HN3","created_at":"2026-07-05T08:53:59Z"},{"alias_kind":"pith_short_8","alias_value":"TRJGNWJW","created_at":"2026-07-05T08:53:59Z"}],"graph_snapshots":[{"event_id":"sha256:46288604c74203f5ee3d2c00c3a105289a10f682fc32787bf8ae2b34b24e6713","target":"graph","created_at":"2026-07-05T08:53:59Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2110.03173/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In contrast to single-objective optimization (SOO), multi-objective optimization (MOO) requires an optimizer to find the Pareto frontier, a subset of feasible solutions that are not dominated by other feasible solutions. In this paper, we propose LaMOO, a novel multi-objective optimizer that learns a model from observed samples to partition the search space and then focus on promising regions that are likely to contain a subset of the Pareto frontier. The partitioning is based on the dominance number, which measures \"how close\" a data point is to the Pareto frontier among existing samples. To ","authors_text":"Kevin Yang, Linnan Wang, Tian Guo, Tianjun Zhang, Yiyang Zhao, Yuandong Tian","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-07T03:56:19Z","title":"Multi-objective Optimization by Learning Space Partitions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.03173","kind":"arxiv","version":4},"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":"sha256:2d285dad3651f0519a3ce40262909746dccbc15b7e12e9f097e982a67ee2bd72","target":"record","created_at":"2026-07-05T08:53:59Z","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":"3a3ba077e0d2d9290f61317ec0209a19f2ccb11d35b36b149af79776d113f187","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-07T03:56:19Z","title_canon_sha256":"4de45b7b9518225369dc4abfc6f7fb3aaafcd92dbc86754a7242156764cbfc00"},"schema_version":"1.0","source":{"id":"2110.03173","kind":"arxiv","version":4}},"canonical_sha256":"9c5266d936c92d2f9dbbc5a3122e59dbd2b1df01bb21c4af79e2853f155a537d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9c5266d936c92d2f9dbbc5a3122e59dbd2b1df01bb21c4af79e2853f155a537d","first_computed_at":"2026-07-05T08:53:59.838477Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:53:59.838477Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"x1Ic1FgPPXVLeap9sTV9qJv/66jOsHqjQ4yxczeznsKYqSUBSuQyaJ2Z2MX2nbcILMQt78o6gTQQgnaPT1JaCA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:53:59.838918Z","signed_message":"canonical_sha256_bytes"},"source_id":"2110.03173","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2d285dad3651f0519a3ce40262909746dccbc15b7e12e9f097e982a67ee2bd72","sha256:46288604c74203f5ee3d2c00c3a105289a10f682fc32787bf8ae2b34b24e6713"],"state_sha256":"7fc8fd05b59f889bbac717d96b54476385b66b05f80e3bfcb0c1f73faf4259ef"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A+B1clNWb3Id6YWqv8bB8swyHRIPx5094l54bC2Hfs3TTeFCtNGI2OWf9AzTp4QgD2mwop3f5Jb0T9AGI+OMCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T00:38:30.853692Z","bundle_sha256":"a408dda2f1e12bf8128b43822a6d729f8fd0d88229694528029a4cc6f6d2614f"}}