{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:6XDQMPCH3QBBDOLQZ3ZCEHHVL6","short_pith_number":"pith:6XDQMPCH","canonical_record":{"source":{"id":"2006.00767","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2020-06-01T07:55:16Z","cross_cats_sorted":[],"title_canon_sha256":"4d834d715f9f1278b3a800d88b16576124a40bbece3435df250b31beb6f39f1f","abstract_canon_sha256":"4dc0ed3209af878cfcb93a267b31f49014d8a35ed638928a1375f0caba446551"},"schema_version":"1.0"},"canonical_sha256":"f5c7063c47dc0211b970cef2221cf55fba892f6e2226db84c825298e0c116283","source":{"kind":"arxiv","id":"2006.00767","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.00767","created_at":"2026-07-05T07:01:23Z"},{"alias_kind":"arxiv_version","alias_value":"2006.00767v3","created_at":"2026-07-05T07:01:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.00767","created_at":"2026-07-05T07:01:23Z"},{"alias_kind":"pith_short_12","alias_value":"6XDQMPCH3QBB","created_at":"2026-07-05T07:01:23Z"},{"alias_kind":"pith_short_16","alias_value":"6XDQMPCH3QBBDOLQ","created_at":"2026-07-05T07:01:23Z"},{"alias_kind":"pith_short_8","alias_value":"6XDQMPCH","created_at":"2026-07-05T07:01:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:6XDQMPCH3QBBDOLQZ3ZCEHHVL6","target":"record","payload":{"canonical_record":{"source":{"id":"2006.00767","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2020-06-01T07:55:16Z","cross_cats_sorted":[],"title_canon_sha256":"4d834d715f9f1278b3a800d88b16576124a40bbece3435df250b31beb6f39f1f","abstract_canon_sha256":"4dc0ed3209af878cfcb93a267b31f49014d8a35ed638928a1375f0caba446551"},"schema_version":"1.0"},"canonical_sha256":"f5c7063c47dc0211b970cef2221cf55fba892f6e2226db84c825298e0c116283","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:01:23.157126Z","signature_b64":"EF5p+OYzSezxYq8TzxF4VLPpYF7nC1Eq09J827/kvcTh0r4X9KcfSnf124toOivQm0GyU0gLbrG9Kf5dkocoBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f5c7063c47dc0211b970cef2221cf55fba892f6e2226db84c825298e0c116283","last_reissued_at":"2026-07-05T07:01:23.156552Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:01:23.156552Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2006.00767","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":"2026-07-05T07:01:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GefZmXIIaJYDlKoalBhb/EFKG4AXJm4PaEyVr8nLLYJlCJQUzJ3lIfZtM//Jj5L1GtC2159zYKnuwtysekhAAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T12:36:05.581493Z"},"content_sha256":"bef60b928f3f1bc1cb20d9b0e92a0319c9994af89b77c4509bfa22d5f1ba0230","schema_version":"1.0","event_id":"sha256:bef60b928f3f1bc1cb20d9b0e92a0319c9994af89b77c4509bfa22d5f1ba0230"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:6XDQMPCH3QBBDOLQZ3ZCEHHVL6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Generative Multiple-purpose Sampler for Weighted M-estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Jun S Liu, Minsuk Shin, Shijie Wang","submitted_at":"2020-06-01T07:55:16Z","abstract_excerpt":"To overcome the computational bottleneck of various data perturbation procedures such as the bootstrap and cross validations, we propose the Generative Multiple-purpose Sampler (GMS), which constructs a generator function to produce solutions of weighted M-estimators from a set of given weights and tuning parameters. The GMS is implemented by a single optimization without having to repeatedly evaluate the minimizers of weighted losses, and is thus capable of significantly reducing the computational time. We demonstrate that the GMS framework enables the implementation of various statistical pr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.00767","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2006.00767/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":"2026-07-05T07:01:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tllcjt9BwKtgAoDGeWai2G36gBLg2ChDuU18q3amXvsBv17bgJXqWsN0OCUel7qqp1fwD2FR86Ut8X/2zyC2Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T12:36:05.582016Z"},"content_sha256":"4d036070502b157221fb05ff89cd4e7300a4cd62b1403fdfc37e80715a0d1d40","schema_version":"1.0","event_id":"sha256:4d036070502b157221fb05ff89cd4e7300a4cd62b1403fdfc37e80715a0d1d40"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6XDQMPCH3QBBDOLQZ3ZCEHHVL6/bundle.json","state_url":"https://pith.science/pith/6XDQMPCH3QBBDOLQZ3ZCEHHVL6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6XDQMPCH3QBBDOLQZ3ZCEHHVL6/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":"2026-08-04T12:36:05Z","links":{"resolver":"https://pith.science/pith/6XDQMPCH3QBBDOLQZ3ZCEHHVL6","bundle":"https://pith.science/pith/6XDQMPCH3QBBDOLQZ3ZCEHHVL6/bundle.json","state":"https://pith.science/pith/6XDQMPCH3QBBDOLQZ3ZCEHHVL6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6XDQMPCH3QBBDOLQZ3ZCEHHVL6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:6XDQMPCH3QBBDOLQZ3ZCEHHVL6","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":"4dc0ed3209af878cfcb93a267b31f49014d8a35ed638928a1375f0caba446551","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2020-06-01T07:55:16Z","title_canon_sha256":"4d834d715f9f1278b3a800d88b16576124a40bbece3435df250b31beb6f39f1f"},"schema_version":"1.0","source":{"id":"2006.00767","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.00767","created_at":"2026-07-05T07:01:23Z"},{"alias_kind":"arxiv_version","alias_value":"2006.00767v3","created_at":"2026-07-05T07:01:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.00767","created_at":"2026-07-05T07:01:23Z"},{"alias_kind":"pith_short_12","alias_value":"6XDQMPCH3QBB","created_at":"2026-07-05T07:01:23Z"},{"alias_kind":"pith_short_16","alias_value":"6XDQMPCH3QBBDOLQ","created_at":"2026-07-05T07:01:23Z"},{"alias_kind":"pith_short_8","alias_value":"6XDQMPCH","created_at":"2026-07-05T07:01:23Z"}],"graph_snapshots":[{"event_id":"sha256:4d036070502b157221fb05ff89cd4e7300a4cd62b1403fdfc37e80715a0d1d40","target":"graph","created_at":"2026-07-05T07:01:23Z","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/2006.00767/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"To overcome the computational bottleneck of various data perturbation procedures such as the bootstrap and cross validations, we propose the Generative Multiple-purpose Sampler (GMS), which constructs a generator function to produce solutions of weighted M-estimators from a set of given weights and tuning parameters. The GMS is implemented by a single optimization without having to repeatedly evaluate the minimizers of weighted losses, and is thus capable of significantly reducing the computational time. We demonstrate that the GMS framework enables the implementation of various statistical pr","authors_text":"Jun S Liu, Minsuk Shin, Shijie Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2020-06-01T07:55:16Z","title":"Generative Multiple-purpose Sampler for Weighted M-estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.00767","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:bef60b928f3f1bc1cb20d9b0e92a0319c9994af89b77c4509bfa22d5f1ba0230","target":"record","created_at":"2026-07-05T07:01:23Z","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":"4dc0ed3209af878cfcb93a267b31f49014d8a35ed638928a1375f0caba446551","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2020-06-01T07:55:16Z","title_canon_sha256":"4d834d715f9f1278b3a800d88b16576124a40bbece3435df250b31beb6f39f1f"},"schema_version":"1.0","source":{"id":"2006.00767","kind":"arxiv","version":3}},"canonical_sha256":"f5c7063c47dc0211b970cef2221cf55fba892f6e2226db84c825298e0c116283","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f5c7063c47dc0211b970cef2221cf55fba892f6e2226db84c825298e0c116283","first_computed_at":"2026-07-05T07:01:23.156552Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:01:23.156552Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EF5p+OYzSezxYq8TzxF4VLPpYF7nC1Eq09J827/kvcTh0r4X9KcfSnf124toOivQm0GyU0gLbrG9Kf5dkocoBw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:01:23.157126Z","signed_message":"canonical_sha256_bytes"},"source_id":"2006.00767","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bef60b928f3f1bc1cb20d9b0e92a0319c9994af89b77c4509bfa22d5f1ba0230","sha256:4d036070502b157221fb05ff89cd4e7300a4cd62b1403fdfc37e80715a0d1d40"],"state_sha256":"9b97d83bc5df3531bd40b50403db0225dd7ce2641223480abe98274dce548c49"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"orHjyoV2Ktr1GbBfCqa850W17PMF7dA1lkwXNu8RiR8wzCSyk23thMtFKrkwTSmh3P9uLQERHEYac9OegPaMBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T12:36:05.587628Z","bundle_sha256":"a66cc47fa8aba45e47c4ef6e5075afc638c89e7e0e1f346b36f36debcdeb62c5"}}