{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:A6AMTIO6GJQOXAPGUZIMFGIPCG","short_pith_number":"pith:A6AMTIO6","schema_version":"1.0","canonical_sha256":"0780c9a1de3260eb81e6a650c2990f11be4da1bf4fc6897610d38d1b23d00aaa","source":{"kind":"arxiv","id":"1902.03736","version":1},"attestation_state":"computed","paper":{"title":"A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"math.PR","authors_text":"Chi Jin, Michael I. Jordan, Praneeth Netrapalli, Rong Ge, Sham M. Kakade","submitted_at":"2019-02-11T05:41:28Z","abstract_excerpt":"In this note, we derive concentration inequalities for random vectors with subGaussian norm (a generalization of both subGaussian random vectors and norm bounded random vectors), which are tight up to logarithmic factors."},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1902.03736","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.PR","submitted_at":"2019-02-11T05:41:28Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"ed6d8d74fb500d2ee939d2b3e3384c7938f9934626552f5e2e5bf576cd925d11","abstract_canon_sha256":"0916500dbc52dbfacccbc0433c2e9670f0a9dbfc8f9bd53584fbc96f66aca22a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:54:19.699194Z","signature_b64":"9Qv08CIA2T7JE6JuadRxNSCRSh+tyxLegWs8d3T638zjB0j4JPNJS/oKBE17i2+iLV1uSLloGY9+Mx1Wo+sOBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0780c9a1de3260eb81e6a650c2990f11be4da1bf4fc6897610d38d1b23d00aaa","last_reissued_at":"2026-05-17T23:54:19.698522Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:54:19.698522Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"math.PR","authors_text":"Chi Jin, Michael I. Jordan, Praneeth Netrapalli, Rong Ge, Sham M. Kakade","submitted_at":"2019-02-11T05:41:28Z","abstract_excerpt":"In this note, we derive concentration inequalities for random vectors with subGaussian norm (a generalization of both subGaussian random vectors and norm bounded random vectors), which are tight up to logarithmic factors."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.03736","kind":"arxiv","version":1},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1902.03736","created_at":"2026-05-17T23:54:19.698621+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.03736v1","created_at":"2026-05-17T23:54:19.698621+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.03736","created_at":"2026-05-17T23:54:19.698621+00:00"},{"alias_kind":"pith_short_12","alias_value":"A6AMTIO6GJQO","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"A6AMTIO6GJQOXAPG","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"A6AMTIO6","created_at":"2026-05-18T12:33:12.712433+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":7,"sample":[{"citing_arxiv_id":"2510.06141","citing_title":"High-Probability Convergence Guarantees of Decentralized SGD","ref_index":76,"is_internal_anchor":true},{"citing_arxiv_id":"2512.12572","citing_title":"On the Accuracy of Newton Step and Influence Function Data Attributions","ref_index":12,"is_internal_anchor":true},{"citing_arxiv_id":"2605.20999","citing_title":"Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise","ref_index":21,"is_internal_anchor":true},{"citing_arxiv_id":"2506.00158","citing_title":"Privacy Amplification in Differentially Private Zeroth-Order Optimization with Hidden States","ref_index":13,"is_internal_anchor":true},{"citing_arxiv_id":"2506.03074","citing_title":"GL-LowPopArt: A Nearly Instance-Wise Minimax-Optimal Estimator for Generalized Low-Rank Trace Regression","ref_index":5,"is_internal_anchor":true},{"citing_arxiv_id":"2506.03074","citing_title":"GL-LowPopArt: A Nearly Instance-Wise Minimax-Optimal Estimator for Generalized Low-Rank Trace Regression","ref_index":6,"is_internal_anchor":true},{"citing_arxiv_id":"2510.06141","citing_title":"High-Probability Convergence Guarantees of Decentralized SGD","ref_index":76,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/A6AMTIO6GJQOXAPGUZIMFGIPCG","json":"https://pith.science/pith/A6AMTIO6GJQOXAPGUZIMFGIPCG.json","graph_json":"https://pith.science/api/pith-number/A6AMTIO6GJQOXAPGUZIMFGIPCG/graph.json","events_json":"https://pith.science/api/pith-number/A6AMTIO6GJQOXAPGUZIMFGIPCG/events.json","paper":"https://pith.science/paper/A6AMTIO6"},"agent_actions":{"view_html":"https://pith.science/pith/A6AMTIO6GJQOXAPGUZIMFGIPCG","download_json":"https://pith.science/pith/A6AMTIO6GJQOXAPGUZIMFGIPCG.json","view_paper":"https://pith.science/paper/A6AMTIO6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.03736&json=true","fetch_graph":"https://pith.science/api/pith-number/A6AMTIO6GJQOXAPGUZIMFGIPCG/graph.json","fetch_events":"https://pith.science/api/pith-number/A6AMTIO6GJQOXAPGUZIMFGIPCG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A6AMTIO6GJQOXAPGUZIMFGIPCG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A6AMTIO6GJQOXAPGUZIMFGIPCG/action/storage_attestation","attest_author":"https://pith.science/pith/A6AMTIO6GJQOXAPGUZIMFGIPCG/action/author_attestation","sign_citation":"https://pith.science/pith/A6AMTIO6GJQOXAPGUZIMFGIPCG/action/citation_signature","submit_replication":"https://pith.science/pith/A6AMTIO6GJQOXAPGUZIMFGIPCG/action/replication_record"}},"created_at":"2026-05-17T23:54:19.698621+00:00","updated_at":"2026-05-17T23:54:19.698621+00:00"}