{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2010:QGW3NR4V7YCMDZKXKRTAIZBAVD","short_pith_number":"pith:QGW3NR4V","canonical_record":{"source":{"id":"1001.1919","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2010-01-12T15:44:16Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"e67ff806807d95e40d16ca887d097d11bb0e0542c010d274eb6476d6cbc43fd0","abstract_canon_sha256":"88a978939249903c26635a636602896c38bc439e447b5930ce29279aeacb9afe"},"schema_version":"1.0"},"canonical_sha256":"81adb6c795fe04c1e5575466046420a8c8503f2389a85a809132ec9361d24f91","source":{"kind":"arxiv","id":"1001.1919","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1001.1919","created_at":"2026-05-18T04:31:13Z"},{"alias_kind":"arxiv_version","alias_value":"1001.1919v2","created_at":"2026-05-18T04:31:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1001.1919","created_at":"2026-05-18T04:31:13Z"},{"alias_kind":"pith_short_12","alias_value":"QGW3NR4V7YCM","created_at":"2026-05-18T12:26:12Z"},{"alias_kind":"pith_short_16","alias_value":"QGW3NR4V7YCMDZKX","created_at":"2026-05-18T12:26:12Z"},{"alias_kind":"pith_short_8","alias_value":"QGW3NR4V","created_at":"2026-05-18T12:26:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2010:QGW3NR4V7YCMDZKXKRTAIZBAVD","target":"record","payload":{"canonical_record":{"source":{"id":"1001.1919","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2010-01-12T15:44:16Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"e67ff806807d95e40d16ca887d097d11bb0e0542c010d274eb6476d6cbc43fd0","abstract_canon_sha256":"88a978939249903c26635a636602896c38bc439e447b5930ce29279aeacb9afe"},"schema_version":"1.0"},"canonical_sha256":"81adb6c795fe04c1e5575466046420a8c8503f2389a85a809132ec9361d24f91","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:31:13.187904Z","signature_b64":"AogRmpCbD1CK+IWdUuO0OILN7YrU0PQ1IGYWefUUYFUi+7Q3TPvMsI2rmtjqXGT5ElZKu+vaFnqhn14BCQghBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"81adb6c795fe04c1e5575466046420a8c8503f2389a85a809132ec9361d24f91","last_reissued_at":"2026-05-18T04:31:13.187436Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:31:13.187436Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1001.1919","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T04:31:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KErRehRkGzA9nJxqJOM1jzzN8W/8zgfv8Dy/5hcTdWkiBKrcenEnB8Q/aigcodkhNaVGO/2KnCsmZxoUDZ3AAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T13:43:36.004713Z"},"content_sha256":"5238701697d9022af7fcfb47e833a1a29c1acb5a88afa2822e75460bcb718deb","schema_version":"1.0","event_id":"sha256:5238701697d9022af7fcfb47e833a1a29c1acb5a88afa2822e75460bcb718deb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2010:QGW3NR4V7YCMDZKXKRTAIZBAVD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Out of Leaders","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Dominique Picard (PMA), Karine Tribouley (PMA, Mathilde Mougeot (PMA), Modal'x)","submitted_at":"2010-01-12T15:44:16Z","abstract_excerpt":"This paper investigates the estimation problem in a regression-type model. To be able to deal with potential high dimensions, we provide a procedure called LOL, for Learning Out of Leaders with no optimization step. LOL is an auto-driven algorithm with two thresholding steps. A first adaptive thresholding helps to select leaders among the initial regressors in order to obtain a first reduction of dimensionality. Then a second thresholding is performed on the linear regression upon the leaders. The consistency of the procedure is investigated. Exponential bounds are obtained, leading to minimax"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1001.1919","kind":"arxiv","version":2},"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":"2026-05-18T04:31:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iB+J7UgD3xUtoJSwmVh8ovOJM1VRDaypcVbTYi1JdKcFlbuIHz6SiSy7DV54tJOxriq35PPolIfSu7/SpQMgBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T13:43:36.005449Z"},"content_sha256":"488edcbefa8ae98c652289e0c51f3e4093efb56d7b4e938fafbcfb41c0b18dca","schema_version":"1.0","event_id":"sha256:488edcbefa8ae98c652289e0c51f3e4093efb56d7b4e938fafbcfb41c0b18dca"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QGW3NR4V7YCMDZKXKRTAIZBAVD/bundle.json","state_url":"https://pith.science/pith/QGW3NR4V7YCMDZKXKRTAIZBAVD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QGW3NR4V7YCMDZKXKRTAIZBAVD/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":"2026-05-23T13:43:36Z","links":{"resolver":"https://pith.science/pith/QGW3NR4V7YCMDZKXKRTAIZBAVD","bundle":"https://pith.science/pith/QGW3NR4V7YCMDZKXKRTAIZBAVD/bundle.json","state":"https://pith.science/pith/QGW3NR4V7YCMDZKXKRTAIZBAVD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QGW3NR4V7YCMDZKXKRTAIZBAVD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2010:QGW3NR4V7YCMDZKXKRTAIZBAVD","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":"88a978939249903c26635a636602896c38bc439e447b5930ce29279aeacb9afe","cross_cats_sorted":["stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2010-01-12T15:44:16Z","title_canon_sha256":"e67ff806807d95e40d16ca887d097d11bb0e0542c010d274eb6476d6cbc43fd0"},"schema_version":"1.0","source":{"id":"1001.1919","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1001.1919","created_at":"2026-05-18T04:31:13Z"},{"alias_kind":"arxiv_version","alias_value":"1001.1919v2","created_at":"2026-05-18T04:31:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1001.1919","created_at":"2026-05-18T04:31:13Z"},{"alias_kind":"pith_short_12","alias_value":"QGW3NR4V7YCM","created_at":"2026-05-18T12:26:12Z"},{"alias_kind":"pith_short_16","alias_value":"QGW3NR4V7YCMDZKX","created_at":"2026-05-18T12:26:12Z"},{"alias_kind":"pith_short_8","alias_value":"QGW3NR4V","created_at":"2026-05-18T12:26:12Z"}],"graph_snapshots":[{"event_id":"sha256:488edcbefa8ae98c652289e0c51f3e4093efb56d7b4e938fafbcfb41c0b18dca","target":"graph","created_at":"2026-05-18T04:31:13Z","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":"This paper investigates the estimation problem in a regression-type model. To be able to deal with potential high dimensions, we provide a procedure called LOL, for Learning Out of Leaders with no optimization step. LOL is an auto-driven algorithm with two thresholding steps. A first adaptive thresholding helps to select leaders among the initial regressors in order to obtain a first reduction of dimensionality. Then a second thresholding is performed on the linear regression upon the leaders. The consistency of the procedure is investigated. Exponential bounds are obtained, leading to minimax","authors_text":"Dominique Picard (PMA), Karine Tribouley (PMA, Mathilde Mougeot (PMA), Modal'x)","cross_cats":["stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2010-01-12T15:44:16Z","title":"Learning Out of Leaders"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1001.1919","kind":"arxiv","version":2},"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":"sha256:5238701697d9022af7fcfb47e833a1a29c1acb5a88afa2822e75460bcb718deb","target":"record","created_at":"2026-05-18T04:31:13Z","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":"88a978939249903c26635a636602896c38bc439e447b5930ce29279aeacb9afe","cross_cats_sorted":["stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2010-01-12T15:44:16Z","title_canon_sha256":"e67ff806807d95e40d16ca887d097d11bb0e0542c010d274eb6476d6cbc43fd0"},"schema_version":"1.0","source":{"id":"1001.1919","kind":"arxiv","version":2}},"canonical_sha256":"81adb6c795fe04c1e5575466046420a8c8503f2389a85a809132ec9361d24f91","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"81adb6c795fe04c1e5575466046420a8c8503f2389a85a809132ec9361d24f91","first_computed_at":"2026-05-18T04:31:13.187436Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T04:31:13.187436Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AogRmpCbD1CK+IWdUuO0OILN7YrU0PQ1IGYWefUUYFUi+7Q3TPvMsI2rmtjqXGT5ElZKu+vaFnqhn14BCQghBA==","signature_status":"signed_v1","signed_at":"2026-05-18T04:31:13.187904Z","signed_message":"canonical_sha256_bytes"},"source_id":"1001.1919","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5238701697d9022af7fcfb47e833a1a29c1acb5a88afa2822e75460bcb718deb","sha256:488edcbefa8ae98c652289e0c51f3e4093efb56d7b4e938fafbcfb41c0b18dca"],"state_sha256":"f0b9b530a64314341798b14a9aabe28d1651ff26545d4d80a0cea15ccd2f2c1f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9aEt4Em50WbnlDx5A2hlV57s0O0HPd3b2JGUpSZ7i3lMRpG6sJa1zwv1GmRMC1iEkQ23BtRCIAVpclJpsJeWDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-23T13:43:36.009203Z","bundle_sha256":"9d58a6b2e0c86789d23027240b98e93f9dfd25a6951dd611c69e30247b8f87ee"}}