{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:6TOIDCDVJIWWYCGLCNALLANOOP","short_pith_number":"pith:6TOIDCDV","canonical_record":{"source":{"id":"1509.01004","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-09-03T09:35:48Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c3a4e486f4e34a6067de7d8224f5ae7929ebf16080876e8e195458815e570981","abstract_canon_sha256":"cf21d30fc24e56ed778b1e0cc2c9b96a1e36e4b2fb7c46bedbba005773a75d06"},"schema_version":"1.0"},"canonical_sha256":"f4dc8188754a2d6c08cb1340b581ae73ec69c31d679474e6cbfd5e591c2b6dfb","source":{"kind":"arxiv","id":"1509.01004","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1509.01004","created_at":"2026-05-18T01:30:57Z"},{"alias_kind":"arxiv_version","alias_value":"1509.01004v2","created_at":"2026-05-18T01:30:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1509.01004","created_at":"2026-05-18T01:30:57Z"},{"alias_kind":"pith_short_12","alias_value":"6TOIDCDVJIWW","created_at":"2026-05-18T12:29:07Z"},{"alias_kind":"pith_short_16","alias_value":"6TOIDCDVJIWWYCGL","created_at":"2026-05-18T12:29:07Z"},{"alias_kind":"pith_short_8","alias_value":"6TOIDCDV","created_at":"2026-05-18T12:29:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:6TOIDCDVJIWWYCGLCNALLANOOP","target":"record","payload":{"canonical_record":{"source":{"id":"1509.01004","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-09-03T09:35:48Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c3a4e486f4e34a6067de7d8224f5ae7929ebf16080876e8e195458815e570981","abstract_canon_sha256":"cf21d30fc24e56ed778b1e0cc2c9b96a1e36e4b2fb7c46bedbba005773a75d06"},"schema_version":"1.0"},"canonical_sha256":"f4dc8188754a2d6c08cb1340b581ae73ec69c31d679474e6cbfd5e591c2b6dfb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:30:57.573989Z","signature_b64":"pSPKQTl40hdJxW9FdoLMGb8hGN83qM0gkAEeobJpZIvOl14dLPuGGVXOzuK0bwScIOQ55SP4tY/HrkfkxgmECw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f4dc8188754a2d6c08cb1340b581ae73ec69c31d679474e6cbfd5e591c2b6dfb","last_reissued_at":"2026-05-18T01:30:57.573323Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:30:57.573323Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1509.01004","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":"2026-05-18T01:30:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JCvjY8PTvCA9canZ8dSFTDNa1BLjRqeE5n2dwBYtAOfpbBQ5vQ0mBH1VrkD3EBN0+yL7eODTj8OENwy1G3mQDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T15:23:36.317766Z"},"content_sha256":"9aaddc67090f7831f7c6e7aa53903536eac821f0366cfdb7b60ddad8ba520f89","schema_version":"1.0","event_id":"sha256:9aaddc67090f7831f7c6e7aa53903536eac821f0366cfdb7b60ddad8ba520f89"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:6TOIDCDVJIWWYCGLCNALLANOOP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bayesian Masking: Sparse Bayesian Estimation with Weaker Shrinkage Bias","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Kohei Hayashi, Shin-ichi Maeda, Yohei Kondo","submitted_at":"2015-09-03T09:35:48Z","abstract_excerpt":"A common strategy for sparse linear regression is to introduce regularization, which eliminates irrelevant features by letting the corresponding weights be zeros. However, regularization often shrinks the estimator for relevant features, which leads to incorrect feature selection. Motivated by the above-mentioned issue, we propose Bayesian masking (BM), a sparse estimation method which imposes no regularization on the weights. The key concept of BM is to introduce binary latent variables that randomly mask features. Estimating the masking rates determines the relevance of the features automati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1509.01004","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":"2026-05-18T01:30:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6Vv2PBum6Rs2OyaV+uwyhTXv32zdtmg+sHvXYCOltEJXDSkQ+wQ/5IiHBsuuf794JMsI1J2kEbRuRaFiK525DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T15:23:36.318162Z"},"content_sha256":"59f67a268a2312f5b15d53cb76421197ebfbbf5884deb715007bcb58ef87fff0","schema_version":"1.0","event_id":"sha256:59f67a268a2312f5b15d53cb76421197ebfbbf5884deb715007bcb58ef87fff0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6TOIDCDVJIWWYCGLCNALLANOOP/bundle.json","state_url":"https://pith.science/pith/6TOIDCDVJIWWYCGLCNALLANOOP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6TOIDCDVJIWWYCGLCNALLANOOP/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":"2026-05-25T15:23:36Z","links":{"resolver":"https://pith.science/pith/6TOIDCDVJIWWYCGLCNALLANOOP","bundle":"https://pith.science/pith/6TOIDCDVJIWWYCGLCNALLANOOP/bundle.json","state":"https://pith.science/pith/6TOIDCDVJIWWYCGLCNALLANOOP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6TOIDCDVJIWWYCGLCNALLANOOP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:6TOIDCDVJIWWYCGLCNALLANOOP","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":"cf21d30fc24e56ed778b1e0cc2c9b96a1e36e4b2fb7c46bedbba005773a75d06","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-09-03T09:35:48Z","title_canon_sha256":"c3a4e486f4e34a6067de7d8224f5ae7929ebf16080876e8e195458815e570981"},"schema_version":"1.0","source":{"id":"1509.01004","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1509.01004","created_at":"2026-05-18T01:30:57Z"},{"alias_kind":"arxiv_version","alias_value":"1509.01004v2","created_at":"2026-05-18T01:30:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1509.01004","created_at":"2026-05-18T01:30:57Z"},{"alias_kind":"pith_short_12","alias_value":"6TOIDCDVJIWW","created_at":"2026-05-18T12:29:07Z"},{"alias_kind":"pith_short_16","alias_value":"6TOIDCDVJIWWYCGL","created_at":"2026-05-18T12:29:07Z"},{"alias_kind":"pith_short_8","alias_value":"6TOIDCDV","created_at":"2026-05-18T12:29:07Z"}],"graph_snapshots":[{"event_id":"sha256:59f67a268a2312f5b15d53cb76421197ebfbbf5884deb715007bcb58ef87fff0","target":"graph","created_at":"2026-05-18T01:30:57Z","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":"A common strategy for sparse linear regression is to introduce regularization, which eliminates irrelevant features by letting the corresponding weights be zeros. However, regularization often shrinks the estimator for relevant features, which leads to incorrect feature selection. Motivated by the above-mentioned issue, we propose Bayesian masking (BM), a sparse estimation method which imposes no regularization on the weights. The key concept of BM is to introduce binary latent variables that randomly mask features. Estimating the masking rates determines the relevance of the features automati","authors_text":"Kohei Hayashi, Shin-ichi Maeda, Yohei Kondo","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-09-03T09:35:48Z","title":"Bayesian Masking: Sparse Bayesian Estimation with Weaker Shrinkage Bias"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1509.01004","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":"sha256:9aaddc67090f7831f7c6e7aa53903536eac821f0366cfdb7b60ddad8ba520f89","target":"record","created_at":"2026-05-18T01:30:57Z","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":"cf21d30fc24e56ed778b1e0cc2c9b96a1e36e4b2fb7c46bedbba005773a75d06","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-09-03T09:35:48Z","title_canon_sha256":"c3a4e486f4e34a6067de7d8224f5ae7929ebf16080876e8e195458815e570981"},"schema_version":"1.0","source":{"id":"1509.01004","kind":"arxiv","version":2}},"canonical_sha256":"f4dc8188754a2d6c08cb1340b581ae73ec69c31d679474e6cbfd5e591c2b6dfb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f4dc8188754a2d6c08cb1340b581ae73ec69c31d679474e6cbfd5e591c2b6dfb","first_computed_at":"2026-05-18T01:30:57.573323Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:30:57.573323Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pSPKQTl40hdJxW9FdoLMGb8hGN83qM0gkAEeobJpZIvOl14dLPuGGVXOzuK0bwScIOQ55SP4tY/HrkfkxgmECw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:30:57.573989Z","signed_message":"canonical_sha256_bytes"},"source_id":"1509.01004","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9aaddc67090f7831f7c6e7aa53903536eac821f0366cfdb7b60ddad8ba520f89","sha256:59f67a268a2312f5b15d53cb76421197ebfbbf5884deb715007bcb58ef87fff0"],"state_sha256":"e819f8bc242983f2a20ff3f12707bd255085d47f56201356ef136ae7bba27996"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d6Pas1zmfoHhM+9j1Hysv4jB4/Iwn1M1Ytq4Xb1QlN8kE38CSgDQB/nhjYwpMVAzFHMiKi2zZLhHpbVEc6ICCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T15:23:36.321759Z","bundle_sha256":"19575d7837037d5c70ab26093894e50820910c984e21ae8930d21d84740cdfe5"}}