{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2010:TRN3O4MMVSOO7FWXRJKFI45XMT","short_pith_number":"pith:TRN3O4MM","canonical_record":{"source":{"id":"1009.2707","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2010-09-14T16:17:29Z","cross_cats_sorted":["stat.CO","stat.TH"],"title_canon_sha256":"286bcb934ab2a89e76c1a598b59e55db64e7db1ed83bec4ef8f91671c3d97bd2","abstract_canon_sha256":"f6919fa2af292830ec4b3542f997ce068d91f62dbb8c29a0ba76aae8f9f8d259"},"schema_version":"1.0"},"canonical_sha256":"9c5bb7718cac9cef96d78a545473b764dea9bc4c69413023c624bb43438597c6","source":{"kind":"arxiv","id":"1009.2707","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1009.2707","created_at":"2026-05-18T04:26:54Z"},{"alias_kind":"arxiv_version","alias_value":"1009.2707v2","created_at":"2026-05-18T04:26:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1009.2707","created_at":"2026-05-18T04:26:54Z"},{"alias_kind":"pith_short_12","alias_value":"TRN3O4MMVSOO","created_at":"2026-05-18T12:26:15Z"},{"alias_kind":"pith_short_16","alias_value":"TRN3O4MMVSOO7FWX","created_at":"2026-05-18T12:26:15Z"},{"alias_kind":"pith_short_8","alias_value":"TRN3O4MM","created_at":"2026-05-18T12:26:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2010:TRN3O4MMVSOO7FWXRJKFI45XMT","target":"record","payload":{"canonical_record":{"source":{"id":"1009.2707","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2010-09-14T16:17:29Z","cross_cats_sorted":["stat.CO","stat.TH"],"title_canon_sha256":"286bcb934ab2a89e76c1a598b59e55db64e7db1ed83bec4ef8f91671c3d97bd2","abstract_canon_sha256":"f6919fa2af292830ec4b3542f997ce068d91f62dbb8c29a0ba76aae8f9f8d259"},"schema_version":"1.0"},"canonical_sha256":"9c5bb7718cac9cef96d78a545473b764dea9bc4c69413023c624bb43438597c6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:26:54.625390Z","signature_b64":"CW1aGhkwMNXldTBvl2B79MD14uax9Fp8Igk2Sj5P5ka1bDseOE/OZi2k8B+2MpM1WoImI5qzkbth//8XepxVCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9c5bb7718cac9cef96d78a545473b764dea9bc4c69413023c624bb43438597c6","last_reissued_at":"2026-05-18T04:26:54.624977Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:26:54.624977Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1009.2707","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":"2026-05-18T04:26:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SE5kumfKticoVPCoeqLATUG/PVzKVt4YNOtEzU6O7s3bvHwho5g49tSLybvdK8sc4map0RS81UAGzj4t7NiCDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T14:25:59.292264Z"},"content_sha256":"a867870de937ecd36c13241dcb8a9218989c9765cd5c17d52bdb7fecd009a667","schema_version":"1.0","event_id":"sha256:a867870de937ecd36c13241dcb8a9218989c9765cd5c17d52bdb7fecd009a667"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2010:TRN3O4MMVSOO7FWXRJKFI45XMT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Pac-bayesian bounds for sparse regression estimation with exponential weights","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO","stat.TH"],"primary_cat":"math.ST","authors_text":"Karim Lounici, Pierre Alquier","submitted_at":"2010-09-14T16:17:29Z","abstract_excerpt":"We consider the sparse regression model where the number of parameters $p$ is larger than the sample size $n$. The difficulty when considering high-dimensional problems is to propose estimators achieving a good compromise between statistical and computational performances. The BIC estimator for instance performs well from the statistical point of view \\cite{BTW07} but can only be computed for values of $p$ of at most a few tens. The Lasso estimator is solution of a convex minimization problem, hence computable for large value of $p$. However stringent conditions on the design are required to e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1009.2707","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":"2026-05-18T04:26:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"txm0vhjdYO301BNNdg5eWIrWnSZDzDW7YCE3e2W4nobGfi/Qzc9byX8D5WgO0DLIHfvcHj+AN9Ak5kXw92rBDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T14:25:59.292664Z"},"content_sha256":"81202270a8fb4eb9b4c71721ff219f5febf6d03ce4f1381b7bd6ae89cc425724","schema_version":"1.0","event_id":"sha256:81202270a8fb4eb9b4c71721ff219f5febf6d03ce4f1381b7bd6ae89cc425724"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TRN3O4MMVSOO7FWXRJKFI45XMT/bundle.json","state_url":"https://pith.science/pith/TRN3O4MMVSOO7FWXRJKFI45XMT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TRN3O4MMVSOO7FWXRJKFI45XMT/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":"2026-06-04T14:25:59Z","links":{"resolver":"https://pith.science/pith/TRN3O4MMVSOO7FWXRJKFI45XMT","bundle":"https://pith.science/pith/TRN3O4MMVSOO7FWXRJKFI45XMT/bundle.json","state":"https://pith.science/pith/TRN3O4MMVSOO7FWXRJKFI45XMT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TRN3O4MMVSOO7FWXRJKFI45XMT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2010:TRN3O4MMVSOO7FWXRJKFI45XMT","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":"f6919fa2af292830ec4b3542f997ce068d91f62dbb8c29a0ba76aae8f9f8d259","cross_cats_sorted":["stat.CO","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2010-09-14T16:17:29Z","title_canon_sha256":"286bcb934ab2a89e76c1a598b59e55db64e7db1ed83bec4ef8f91671c3d97bd2"},"schema_version":"1.0","source":{"id":"1009.2707","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1009.2707","created_at":"2026-05-18T04:26:54Z"},{"alias_kind":"arxiv_version","alias_value":"1009.2707v2","created_at":"2026-05-18T04:26:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1009.2707","created_at":"2026-05-18T04:26:54Z"},{"alias_kind":"pith_short_12","alias_value":"TRN3O4MMVSOO","created_at":"2026-05-18T12:26:15Z"},{"alias_kind":"pith_short_16","alias_value":"TRN3O4MMVSOO7FWX","created_at":"2026-05-18T12:26:15Z"},{"alias_kind":"pith_short_8","alias_value":"TRN3O4MM","created_at":"2026-05-18T12:26:15Z"}],"graph_snapshots":[{"event_id":"sha256:81202270a8fb4eb9b4c71721ff219f5febf6d03ce4f1381b7bd6ae89cc425724","target":"graph","created_at":"2026-05-18T04:26:54Z","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":"We consider the sparse regression model where the number of parameters $p$ is larger than the sample size $n$. The difficulty when considering high-dimensional problems is to propose estimators achieving a good compromise between statistical and computational performances. The BIC estimator for instance performs well from the statistical point of view \\cite{BTW07} but can only be computed for values of $p$ of at most a few tens. The Lasso estimator is solution of a convex minimization problem, hence computable for large value of $p$. However stringent conditions on the design are required to e","authors_text":"Karim Lounici, Pierre Alquier","cross_cats":["stat.CO","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2010-09-14T16:17:29Z","title":"Pac-bayesian bounds for sparse regression estimation with exponential weights"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1009.2707","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:a867870de937ecd36c13241dcb8a9218989c9765cd5c17d52bdb7fecd009a667","target":"record","created_at":"2026-05-18T04:26:54Z","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":"f6919fa2af292830ec4b3542f997ce068d91f62dbb8c29a0ba76aae8f9f8d259","cross_cats_sorted":["stat.CO","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2010-09-14T16:17:29Z","title_canon_sha256":"286bcb934ab2a89e76c1a598b59e55db64e7db1ed83bec4ef8f91671c3d97bd2"},"schema_version":"1.0","source":{"id":"1009.2707","kind":"arxiv","version":2}},"canonical_sha256":"9c5bb7718cac9cef96d78a545473b764dea9bc4c69413023c624bb43438597c6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9c5bb7718cac9cef96d78a545473b764dea9bc4c69413023c624bb43438597c6","first_computed_at":"2026-05-18T04:26:54.624977Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T04:26:54.624977Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CW1aGhkwMNXldTBvl2B79MD14uax9Fp8Igk2Sj5P5ka1bDseOE/OZi2k8B+2MpM1WoImI5qzkbth//8XepxVCA==","signature_status":"signed_v1","signed_at":"2026-05-18T04:26:54.625390Z","signed_message":"canonical_sha256_bytes"},"source_id":"1009.2707","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a867870de937ecd36c13241dcb8a9218989c9765cd5c17d52bdb7fecd009a667","sha256:81202270a8fb4eb9b4c71721ff219f5febf6d03ce4f1381b7bd6ae89cc425724"],"state_sha256":"4b92b45d997ae1cd80dc7f98c6d39d3b21dcb2823159e1ac181dcd8d4a35c351"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"W2mCqIQ1uGgcBEx8pK0CibOaAawJkHJLNIFRvIC/+m91yHAn8SVkyRYzrlE4tIuAiXWj5Lv5s8cDZCjE7H+6BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T14:25:59.295177Z","bundle_sha256":"414aab3463ee40a93450a50ed9f0e9b3118fc673070710f2763617d08974c2ce"}}