{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:OWHKO3VRGHH7MIDJ5NGWLVQNJD","short_pith_number":"pith:OWHKO3VR","canonical_record":{"source":{"id":"1805.10582","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-27T06:12:50Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"9508f707cb1dae7e54373f0f4bb9d1f0aa771a96318f726cfa983e5fe69bbae3","abstract_canon_sha256":"45c08e688dfe471cc5e3e40a7937022156851f3b51bc58628c13334931eadad9"},"schema_version":"1.0"},"canonical_sha256":"758ea76eb131cff62069eb4d65d60d48d8b14c10b30bfaaa67db64cbd0ab5b88","source":{"kind":"arxiv","id":"1805.10582","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.10582","created_at":"2026-05-17T23:43:17Z"},{"alias_kind":"arxiv_version","alias_value":"1805.10582v3","created_at":"2026-05-17T23:43:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.10582","created_at":"2026-05-17T23:43:17Z"},{"alias_kind":"pith_short_12","alias_value":"OWHKO3VRGHH7","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"OWHKO3VRGHH7MIDJ","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"OWHKO3VR","created_at":"2026-05-18T12:32:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:OWHKO3VRGHH7MIDJ5NGWLVQNJD","target":"record","payload":{"canonical_record":{"source":{"id":"1805.10582","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-27T06:12:50Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"9508f707cb1dae7e54373f0f4bb9d1f0aa771a96318f726cfa983e5fe69bbae3","abstract_canon_sha256":"45c08e688dfe471cc5e3e40a7937022156851f3b51bc58628c13334931eadad9"},"schema_version":"1.0"},"canonical_sha256":"758ea76eb131cff62069eb4d65d60d48d8b14c10b30bfaaa67db64cbd0ab5b88","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:17.981995Z","signature_b64":"O1TYKG7OK0YdGqjVy7tvPe0Cly6zHd+AatY5C7ZqDZFRSocpgo53fOecdHx/JYFN5U55GephyK7PsIOWcxKnAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"758ea76eb131cff62069eb4d65d60d48d8b14c10b30bfaaa67db64cbd0ab5b88","last_reissued_at":"2026-05-17T23:43:17.981372Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:17.981372Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.10582","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":"2026-05-17T23:43:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2gAkPMPePcc1RbyDNUrV2yk32vLHOMKNv7Kep6jVtGjAKvl8qoJb51skxt1n3/J5O4UMWIRpQeyBNzCvai4wCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T23:13:06.775636Z"},"content_sha256":"58dc5943d26a721ea3705b9f2cb82391d177f0a47b88b5132f5f3a14baf4f90e","schema_version":"1.0","event_id":"sha256:58dc5943d26a721ea3705b9f2cb82391d177f0a47b88b5132f5f3a14baf4f90e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:OWHKO3VRGHH7MIDJ5NGWLVQNJD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Metric-Optimized Example Weights","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"stat.ML","authors_text":"Harikrishna Narasimhan, Mahdi MIlani Fard, Maya Gupta, Sen Zhao","submitted_at":"2018-05-27T06:12:50Z","abstract_excerpt":"Real-world machine learning applications often have complex test metrics, and may have training and test data that are not identically distributed. Motivated by known connections between complex test metrics and cost-weighted learning, we propose addressing these issues by using a weighted loss function with a standard loss, where the weights on the training examples are learned to optimize the test metric on a validation set. These metric-optimized example weights can be learned for any test metric, including black box and customized ones for specific applications. We illustrate the performan"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.10582","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":"2026-05-17T23:43:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1Pa23j0um5o+VP+We7nW2GZKHdexkZKKVGUzrZeyPKjNR0bXipZVydv6maWGXcHl0PUXKXXER5AcOZVqRiEzDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T23:13:06.775988Z"},"content_sha256":"f4f451282678c035d7a14c99b5742a492910b46126bc98b3e76670542e4ccef6","schema_version":"1.0","event_id":"sha256:f4f451282678c035d7a14c99b5742a492910b46126bc98b3e76670542e4ccef6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OWHKO3VRGHH7MIDJ5NGWLVQNJD/bundle.json","state_url":"https://pith.science/pith/OWHKO3VRGHH7MIDJ5NGWLVQNJD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OWHKO3VRGHH7MIDJ5NGWLVQNJD/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":"2026-06-02T23:13:06Z","links":{"resolver":"https://pith.science/pith/OWHKO3VRGHH7MIDJ5NGWLVQNJD","bundle":"https://pith.science/pith/OWHKO3VRGHH7MIDJ5NGWLVQNJD/bundle.json","state":"https://pith.science/pith/OWHKO3VRGHH7MIDJ5NGWLVQNJD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OWHKO3VRGHH7MIDJ5NGWLVQNJD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:OWHKO3VRGHH7MIDJ5NGWLVQNJD","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":"45c08e688dfe471cc5e3e40a7937022156851f3b51bc58628c13334931eadad9","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-27T06:12:50Z","title_canon_sha256":"9508f707cb1dae7e54373f0f4bb9d1f0aa771a96318f726cfa983e5fe69bbae3"},"schema_version":"1.0","source":{"id":"1805.10582","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.10582","created_at":"2026-05-17T23:43:17Z"},{"alias_kind":"arxiv_version","alias_value":"1805.10582v3","created_at":"2026-05-17T23:43:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.10582","created_at":"2026-05-17T23:43:17Z"},{"alias_kind":"pith_short_12","alias_value":"OWHKO3VRGHH7","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"OWHKO3VRGHH7MIDJ","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"OWHKO3VR","created_at":"2026-05-18T12:32:43Z"}],"graph_snapshots":[{"event_id":"sha256:f4f451282678c035d7a14c99b5742a492910b46126bc98b3e76670542e4ccef6","target":"graph","created_at":"2026-05-17T23:43:17Z","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":"Real-world machine learning applications often have complex test metrics, and may have training and test data that are not identically distributed. Motivated by known connections between complex test metrics and cost-weighted learning, we propose addressing these issues by using a weighted loss function with a standard loss, where the weights on the training examples are learned to optimize the test metric on a validation set. These metric-optimized example weights can be learned for any test metric, including black box and customized ones for specific applications. We illustrate the performan","authors_text":"Harikrishna Narasimhan, Mahdi MIlani Fard, Maya Gupta, Sen Zhao","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-27T06:12:50Z","title":"Metric-Optimized Example Weights"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.10582","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":"sha256:58dc5943d26a721ea3705b9f2cb82391d177f0a47b88b5132f5f3a14baf4f90e","target":"record","created_at":"2026-05-17T23:43:17Z","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":"45c08e688dfe471cc5e3e40a7937022156851f3b51bc58628c13334931eadad9","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-27T06:12:50Z","title_canon_sha256":"9508f707cb1dae7e54373f0f4bb9d1f0aa771a96318f726cfa983e5fe69bbae3"},"schema_version":"1.0","source":{"id":"1805.10582","kind":"arxiv","version":3}},"canonical_sha256":"758ea76eb131cff62069eb4d65d60d48d8b14c10b30bfaaa67db64cbd0ab5b88","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"758ea76eb131cff62069eb4d65d60d48d8b14c10b30bfaaa67db64cbd0ab5b88","first_computed_at":"2026-05-17T23:43:17.981372Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:43:17.981372Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"O1TYKG7OK0YdGqjVy7tvPe0Cly6zHd+AatY5C7ZqDZFRSocpgo53fOecdHx/JYFN5U55GephyK7PsIOWcxKnAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:43:17.981995Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.10582","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:58dc5943d26a721ea3705b9f2cb82391d177f0a47b88b5132f5f3a14baf4f90e","sha256:f4f451282678c035d7a14c99b5742a492910b46126bc98b3e76670542e4ccef6"],"state_sha256":"9d718bff7895da39f609f35399741c0ffa8dbef34dd45574992fbd35dae951b2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kR+lEsboitXn9fUxY8C2ia1Cb3QXxedzwR4qn7fiJJBFfYZn6nAfryKbR6FER/xxIB1pSMVkCTXrlxUvKuwzBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T23:13:06.778057Z","bundle_sha256":"3ab18330c223e3d44ef5ec28225b534de6bf765f1d3caed282277fcf9e71e212"}}