{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:UAF4APEWUZNCNFC2DY3ALKPSVB","short_pith_number":"pith:UAF4APEW","canonical_record":{"source":{"id":"2603.01013","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-03-01T09:47:14Z","cross_cats_sorted":[],"title_canon_sha256":"ed85729ab21e8e2945b4a2b7e34144bd3843dbd753f35b65af053621fe907a25","abstract_canon_sha256":"f388d329b65ed3d42cf082381efcd49bec20e544f591593f76da221f3bf8fcd1"},"schema_version":"1.0"},"canonical_sha256":"a00bc03c96a65a26945a1e3605a9f2a853d7b4fca6ceab7738ab7b3e190202a0","source":{"kind":"arxiv","id":"2603.01013","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2603.01013","created_at":"2026-06-04T01:08:46Z"},{"alias_kind":"arxiv_version","alias_value":"2603.01013v1","created_at":"2026-06-04T01:08:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.01013","created_at":"2026-06-04T01:08:46Z"},{"alias_kind":"pith_short_12","alias_value":"UAF4APEWUZNC","created_at":"2026-06-04T01:08:46Z"},{"alias_kind":"pith_short_16","alias_value":"UAF4APEWUZNCNFC2","created_at":"2026-06-04T01:08:46Z"},{"alias_kind":"pith_short_8","alias_value":"UAF4APEW","created_at":"2026-06-04T01:08:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:UAF4APEWUZNCNFC2DY3ALKPSVB","target":"record","payload":{"canonical_record":{"source":{"id":"2603.01013","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-03-01T09:47:14Z","cross_cats_sorted":[],"title_canon_sha256":"ed85729ab21e8e2945b4a2b7e34144bd3843dbd753f35b65af053621fe907a25","abstract_canon_sha256":"f388d329b65ed3d42cf082381efcd49bec20e544f591593f76da221f3bf8fcd1"},"schema_version":"1.0"},"canonical_sha256":"a00bc03c96a65a26945a1e3605a9f2a853d7b4fca6ceab7738ab7b3e190202a0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-04T01:08:46.593990Z","signature_b64":"QeI9qORdXg2rc2123ldNJwT7e200rGK9jLnaVlYOOFYtsOTZZsDt9FEMvM5qAP07OcJPmqB4G/XBb4zDt6XUBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a00bc03c96a65a26945a1e3605a9f2a853d7b4fca6ceab7738ab7b3e190202a0","last_reissued_at":"2026-06-04T01:08:46.593066Z","signature_status":"signed_v1","first_computed_at":"2026-06-04T01:08:46.593066Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2603.01013","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-04T01:08:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"e6/P+J9JfpR0+8ik3guUJNQXnsIeP6JmRFuZnH4Br70bXC9eoYzKrthwfGusG2wYo5lkWMIVAJE1fNXvjb37AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T08:56:30.713829Z"},"content_sha256":"bcbeb233f9b8840bd1e648ad9443d3044f1d36a2d18d65a0d8072e2dcbe6374b","schema_version":"1.0","event_id":"sha256:bcbeb233f9b8840bd1e648ad9443d3044f1d36a2d18d65a0d8072e2dcbe6374b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:UAF4APEWUZNCNFC2DY3ALKPSVB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Feature-Weighted Maximum Representative Subsampling","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Stefan Kramer, Tony Hauptmann","submitted_at":"2026-03-01T09:47:14Z","abstract_excerpt":"In the social sciences, it is often necessary to debias studies and surveys before valid conclusions can be drawn. Debiasing algorithms enable the computational removal of bias using sample weights. However, an issue arises when only a subset of features is highly biased, while the rest is already representative. Algorithms need to strongly alter the sample distribution to manage a few highly biased features, which can in turn introduce bias into already representative variables. To address this issue, we developed a method that uses feature weights to minimize the impact of highly biased feat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2603.01013","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2603.01013/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":"2026-06-04T01:08:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P9OAkdtI7nrn9XGDW2XTBtRsdtFa6VoeOOKyUq/ZWes83pD/G6Zlwc1/KIzqxQVPQTpqOeeK5BnEmdnhPZTHDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T08:56:30.714209Z"},"content_sha256":"9a7aef45fa2e2c4d1177cfc0769524921b8a5b9b267700070f0a97480d469f22","schema_version":"1.0","event_id":"sha256:9a7aef45fa2e2c4d1177cfc0769524921b8a5b9b267700070f0a97480d469f22"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UAF4APEWUZNCNFC2DY3ALKPSVB/bundle.json","state_url":"https://pith.science/pith/UAF4APEWUZNCNFC2DY3ALKPSVB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UAF4APEWUZNCNFC2DY3ALKPSVB/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":"2026-06-24T08:56:30Z","links":{"resolver":"https://pith.science/pith/UAF4APEWUZNCNFC2DY3ALKPSVB","bundle":"https://pith.science/pith/UAF4APEWUZNCNFC2DY3ALKPSVB/bundle.json","state":"https://pith.science/pith/UAF4APEWUZNCNFC2DY3ALKPSVB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UAF4APEWUZNCNFC2DY3ALKPSVB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:UAF4APEWUZNCNFC2DY3ALKPSVB","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":"f388d329b65ed3d42cf082381efcd49bec20e544f591593f76da221f3bf8fcd1","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-03-01T09:47:14Z","title_canon_sha256":"ed85729ab21e8e2945b4a2b7e34144bd3843dbd753f35b65af053621fe907a25"},"schema_version":"1.0","source":{"id":"2603.01013","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2603.01013","created_at":"2026-06-04T01:08:46Z"},{"alias_kind":"arxiv_version","alias_value":"2603.01013v1","created_at":"2026-06-04T01:08:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.01013","created_at":"2026-06-04T01:08:46Z"},{"alias_kind":"pith_short_12","alias_value":"UAF4APEWUZNC","created_at":"2026-06-04T01:08:46Z"},{"alias_kind":"pith_short_16","alias_value":"UAF4APEWUZNCNFC2","created_at":"2026-06-04T01:08:46Z"},{"alias_kind":"pith_short_8","alias_value":"UAF4APEW","created_at":"2026-06-04T01:08:46Z"}],"graph_snapshots":[{"event_id":"sha256:9a7aef45fa2e2c4d1177cfc0769524921b8a5b9b267700070f0a97480d469f22","target":"graph","created_at":"2026-06-04T01:08:46Z","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/2603.01013/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In the social sciences, it is often necessary to debias studies and surveys before valid conclusions can be drawn. Debiasing algorithms enable the computational removal of bias using sample weights. However, an issue arises when only a subset of features is highly biased, while the rest is already representative. Algorithms need to strongly alter the sample distribution to manage a few highly biased features, which can in turn introduce bias into already representative variables. To address this issue, we developed a method that uses feature weights to minimize the impact of highly biased feat","authors_text":"Stefan Kramer, Tony Hauptmann","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-03-01T09:47:14Z","title":"Feature-Weighted Maximum Representative Subsampling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2603.01013","kind":"arxiv","version":1},"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:bcbeb233f9b8840bd1e648ad9443d3044f1d36a2d18d65a0d8072e2dcbe6374b","target":"record","created_at":"2026-06-04T01:08:46Z","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":"f388d329b65ed3d42cf082381efcd49bec20e544f591593f76da221f3bf8fcd1","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-03-01T09:47:14Z","title_canon_sha256":"ed85729ab21e8e2945b4a2b7e34144bd3843dbd753f35b65af053621fe907a25"},"schema_version":"1.0","source":{"id":"2603.01013","kind":"arxiv","version":1}},"canonical_sha256":"a00bc03c96a65a26945a1e3605a9f2a853d7b4fca6ceab7738ab7b3e190202a0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a00bc03c96a65a26945a1e3605a9f2a853d7b4fca6ceab7738ab7b3e190202a0","first_computed_at":"2026-06-04T01:08:46.593066Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-04T01:08:46.593066Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QeI9qORdXg2rc2123ldNJwT7e200rGK9jLnaVlYOOFYtsOTZZsDt9FEMvM5qAP07OcJPmqB4G/XBb4zDt6XUBQ==","signature_status":"signed_v1","signed_at":"2026-06-04T01:08:46.593990Z","signed_message":"canonical_sha256_bytes"},"source_id":"2603.01013","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bcbeb233f9b8840bd1e648ad9443d3044f1d36a2d18d65a0d8072e2dcbe6374b","sha256:9a7aef45fa2e2c4d1177cfc0769524921b8a5b9b267700070f0a97480d469f22"],"state_sha256":"d150281cbfc9d6f0c6c038155ec640d741cdde745f7c97072abce6b939a475ef"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cARyPZl+HRcVjohG11q0DJu6yPONu/fj2S80GdfGcqvKdzYsO1WV7S8y9sotnqPer/O3CY7djX2ugTGoqNRlAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-24T08:56:30.716257Z","bundle_sha256":"6541724488e60e24a043db45c2a00b363ce47c801d3ccb6587d820b32714b77a"}}