{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:3VMDSXN22MOBCXPAKOBCMVS5IU","short_pith_number":"pith:3VMDSXN2","canonical_record":{"source":{"id":"2210.08571","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2022-10-16T16:01:05Z","cross_cats_sorted":["stat.ML","stat.TH"],"title_canon_sha256":"398e16b9bde8822eff20436deed99734444a9dba291c09fb4254fa6aa930a104","abstract_canon_sha256":"1676193f00a615d84a20932c17a40b701a22eaf9aed06018f89d7d95734498f8"},"schema_version":"1.0"},"canonical_sha256":"dd58395dbad31c115de0538226565d45307f7a72383d7dafe200af279131bb58","source":{"kind":"arxiv","id":"2210.08571","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.08571","created_at":"2026-07-05T11:23:59Z"},{"alias_kind":"arxiv_version","alias_value":"2210.08571v3","created_at":"2026-07-05T11:23:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.08571","created_at":"2026-07-05T11:23:59Z"},{"alias_kind":"pith_short_12","alias_value":"3VMDSXN22MOB","created_at":"2026-07-05T11:23:59Z"},{"alias_kind":"pith_short_16","alias_value":"3VMDSXN22MOBCXPA","created_at":"2026-07-05T11:23:59Z"},{"alias_kind":"pith_short_8","alias_value":"3VMDSXN2","created_at":"2026-07-05T11:23:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:3VMDSXN22MOBCXPAKOBCMVS5IU","target":"record","payload":{"canonical_record":{"source":{"id":"2210.08571","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2022-10-16T16:01:05Z","cross_cats_sorted":["stat.ML","stat.TH"],"title_canon_sha256":"398e16b9bde8822eff20436deed99734444a9dba291c09fb4254fa6aa930a104","abstract_canon_sha256":"1676193f00a615d84a20932c17a40b701a22eaf9aed06018f89d7d95734498f8"},"schema_version":"1.0"},"canonical_sha256":"dd58395dbad31c115de0538226565d45307f7a72383d7dafe200af279131bb58","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:23:59.586685Z","signature_b64":"wAMznD44Pc9k9oLYe1q0pNzHuwKpyaJJQCCqOnIH2EDWZNQV9iRKjOvfBVvxa/s7HGlTUz6huTRjXZMO35rSAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dd58395dbad31c115de0538226565d45307f7a72383d7dafe200af279131bb58","last_reissued_at":"2026-07-05T11:23:59.586067Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:23:59.586067Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2210.08571","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":"2026-07-05T11:23:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wWqOEn8Oz/TezHE21kN+Lq60I+v9Q2habGs/2I2wN0D/ehU6Pdw4HsM3W6OQ67BVwWW3t13IRyMnpxPtAKZmCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T21:02:54.556575Z"},"content_sha256":"c255719fc3a7b90e089b396905c3159fca5c8bbcace1b16bc68ad456394f273e","schema_version":"1.0","event_id":"sha256:c255719fc3a7b90e089b396905c3159fca5c8bbcace1b16bc68ad456394f273e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:3VMDSXN22MOBCXPAKOBCMVS5IU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Dimension free ridge regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Andrea Montanari, Chen Cheng","submitted_at":"2022-10-16T16:01:05Z","abstract_excerpt":"Random matrix theory has become a widely useful tool in high-dimensional statistics and theoretical machine learning. However, random matrix theory is largely focused on the proportional asymptotics in which the number of columns grows proportionally to the number of rows of the data matrix. This is not always the most natural setting in statistics where columns correspond to covariates and rows to samples. With the objective to move beyond the proportional asymptotics, we revisit ridge regression ($\\ell_2$-penalized least squares) on i.i.d. data $(x_i, y_i)$, $i\\le n$, where $x_i$ is a featur"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.08571","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/2210.08571/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":"2026-07-05T11:23:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"x4yPmI3Ty4DoGV1enG3BbHsiywT/CBZAyg6l6+XM1PGzPnEITFd4qUJHTUQDoLz3NN8PMl11xFrDwEh4Zu5bDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T21:02:54.557444Z"},"content_sha256":"423531bd68f805b05c9c590fd865f614ef692244f6d03d276faff31552be94d6","schema_version":"1.0","event_id":"sha256:423531bd68f805b05c9c590fd865f614ef692244f6d03d276faff31552be94d6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3VMDSXN22MOBCXPAKOBCMVS5IU/bundle.json","state_url":"https://pith.science/pith/3VMDSXN22MOBCXPAKOBCMVS5IU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3VMDSXN22MOBCXPAKOBCMVS5IU/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":"2026-08-10T21:02:54Z","links":{"resolver":"https://pith.science/pith/3VMDSXN22MOBCXPAKOBCMVS5IU","bundle":"https://pith.science/pith/3VMDSXN22MOBCXPAKOBCMVS5IU/bundle.json","state":"https://pith.science/pith/3VMDSXN22MOBCXPAKOBCMVS5IU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3VMDSXN22MOBCXPAKOBCMVS5IU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:3VMDSXN22MOBCXPAKOBCMVS5IU","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":"1676193f00a615d84a20932c17a40b701a22eaf9aed06018f89d7d95734498f8","cross_cats_sorted":["stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2022-10-16T16:01:05Z","title_canon_sha256":"398e16b9bde8822eff20436deed99734444a9dba291c09fb4254fa6aa930a104"},"schema_version":"1.0","source":{"id":"2210.08571","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.08571","created_at":"2026-07-05T11:23:59Z"},{"alias_kind":"arxiv_version","alias_value":"2210.08571v3","created_at":"2026-07-05T11:23:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.08571","created_at":"2026-07-05T11:23:59Z"},{"alias_kind":"pith_short_12","alias_value":"3VMDSXN22MOB","created_at":"2026-07-05T11:23:59Z"},{"alias_kind":"pith_short_16","alias_value":"3VMDSXN22MOBCXPA","created_at":"2026-07-05T11:23:59Z"},{"alias_kind":"pith_short_8","alias_value":"3VMDSXN2","created_at":"2026-07-05T11:23:59Z"}],"graph_snapshots":[{"event_id":"sha256:423531bd68f805b05c9c590fd865f614ef692244f6d03d276faff31552be94d6","target":"graph","created_at":"2026-07-05T11:23:59Z","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/2210.08571/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Random matrix theory has become a widely useful tool in high-dimensional statistics and theoretical machine learning. However, random matrix theory is largely focused on the proportional asymptotics in which the number of columns grows proportionally to the number of rows of the data matrix. This is not always the most natural setting in statistics where columns correspond to covariates and rows to samples. With the objective to move beyond the proportional asymptotics, we revisit ridge regression ($\\ell_2$-penalized least squares) on i.i.d. data $(x_i, y_i)$, $i\\le n$, where $x_i$ is a featur","authors_text":"Andrea Montanari, Chen Cheng","cross_cats":["stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2022-10-16T16:01:05Z","title":"Dimension free ridge regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.08571","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:c255719fc3a7b90e089b396905c3159fca5c8bbcace1b16bc68ad456394f273e","target":"record","created_at":"2026-07-05T11:23:59Z","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":"1676193f00a615d84a20932c17a40b701a22eaf9aed06018f89d7d95734498f8","cross_cats_sorted":["stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2022-10-16T16:01:05Z","title_canon_sha256":"398e16b9bde8822eff20436deed99734444a9dba291c09fb4254fa6aa930a104"},"schema_version":"1.0","source":{"id":"2210.08571","kind":"arxiv","version":3}},"canonical_sha256":"dd58395dbad31c115de0538226565d45307f7a72383d7dafe200af279131bb58","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dd58395dbad31c115de0538226565d45307f7a72383d7dafe200af279131bb58","first_computed_at":"2026-07-05T11:23:59.586067Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:23:59.586067Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wAMznD44Pc9k9oLYe1q0pNzHuwKpyaJJQCCqOnIH2EDWZNQV9iRKjOvfBVvxa/s7HGlTUz6huTRjXZMO35rSAA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:23:59.586685Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.08571","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c255719fc3a7b90e089b396905c3159fca5c8bbcace1b16bc68ad456394f273e","sha256:423531bd68f805b05c9c590fd865f614ef692244f6d03d276faff31552be94d6"],"state_sha256":"d43dfa92bbb99a2c0373c3207708e30ddd858106fe59c310921014de0212b66a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gQylr0ahF3lSUt11IAt52AghDAB4atUc0AHuxG0nop/4jGhFdhB579SW2+7Vk9y6CBvsJTMugTbWSWBLFjo7CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T21:02:54.562960Z","bundle_sha256":"fea70ac368f7a9584e4ae218b97832820cfcd14093f9d5832deec2bf98eaa435"}}