{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:4KCFV6SJCFTM5YHPVVEGFCFBBH","short_pith_number":"pith:4KCFV6SJ","schema_version":"1.0","canonical_sha256":"e2845afa491166cee0efad486288a109f34fd559dae3f997168aa801fe5e86d3","source":{"kind":"arxiv","id":"2510.01861","version":2},"attestation_state":"computed","paper":{"title":"Compressed Bayesian Tensor Regression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"stat.ME","authors_text":"Qing Wang, Radu Craiu, Roberto Casarin","submitted_at":"2025-10-02T10:03:11Z","abstract_excerpt":"To address the common problem of high dimensionality in tensor regressions, we introduce a generalized tensor random projection method that embeds high-dimensional tensor-valued covariates into low-dimensional subspaces with minimal loss of information about the responses. The method is flexible, allowing for tensor-wise, mode-wise, or combined random projections as special cases. A Bayesian inference framework is provided featuring the use of a hierarchical prior distribution and a low-rank representation of the parameter. Strong theoretical support is provided for the concentration propertie"},"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":"2510.01861","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2025-10-02T10:03:11Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"6d6828fb496a69c6cc8e2988ec7a655da0956c5f2d768be47ce66764468f2ecd","abstract_canon_sha256":"b42b6dbbbcf249fd01e0d26c4d0e27c43e305a2b181ae91ddeff6fd06631f50f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-11T01:09:18.314209Z","signature_b64":"hHxZ7VoLr6iEkpxGAyNT5CW1wbOPOV1rNFlGrKNOyUW780S/ZhocBtd8gzhXgpekazsJtLVSKk5rI0scsGhSCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e2845afa491166cee0efad486288a109f34fd559dae3f997168aa801fe5e86d3","last_reissued_at":"2026-06-11T01:09:18.313477Z","signature_status":"signed_v1","first_computed_at":"2026-06-11T01:09:18.313477Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Compressed Bayesian Tensor Regression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"stat.ME","authors_text":"Qing Wang, Radu Craiu, Roberto Casarin","submitted_at":"2025-10-02T10:03:11Z","abstract_excerpt":"To address the common problem of high dimensionality in tensor regressions, we introduce a generalized tensor random projection method that embeds high-dimensional tensor-valued covariates into low-dimensional subspaces with minimal loss of information about the responses. The method is flexible, allowing for tensor-wise, mode-wise, or combined random projections as special cases. A Bayesian inference framework is provided featuring the use of a hierarchical prior distribution and a low-rank representation of the parameter. Strong theoretical support is provided for the concentration propertie"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.01861","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2510.01861/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2510.01861","created_at":"2026-06-11T01:09:18.313590+00:00"},{"alias_kind":"arxiv_version","alias_value":"2510.01861v2","created_at":"2026-06-11T01:09:18.313590+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.01861","created_at":"2026-06-11T01:09:18.313590+00:00"},{"alias_kind":"pith_short_12","alias_value":"4KCFV6SJCFTM","created_at":"2026-06-11T01:09:18.313590+00:00"},{"alias_kind":"pith_short_16","alias_value":"4KCFV6SJCFTM5YHP","created_at":"2026-06-11T01:09:18.313590+00:00"},{"alias_kind":"pith_short_8","alias_value":"4KCFV6SJ","created_at":"2026-06-11T01:09:18.313590+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4KCFV6SJCFTM5YHPVVEGFCFBBH","json":"https://pith.science/pith/4KCFV6SJCFTM5YHPVVEGFCFBBH.json","graph_json":"https://pith.science/api/pith-number/4KCFV6SJCFTM5YHPVVEGFCFBBH/graph.json","events_json":"https://pith.science/api/pith-number/4KCFV6SJCFTM5YHPVVEGFCFBBH/events.json","paper":"https://pith.science/paper/4KCFV6SJ"},"agent_actions":{"view_html":"https://pith.science/pith/4KCFV6SJCFTM5YHPVVEGFCFBBH","download_json":"https://pith.science/pith/4KCFV6SJCFTM5YHPVVEGFCFBBH.json","view_paper":"https://pith.science/paper/4KCFV6SJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2510.01861&json=true","fetch_graph":"https://pith.science/api/pith-number/4KCFV6SJCFTM5YHPVVEGFCFBBH/graph.json","fetch_events":"https://pith.science/api/pith-number/4KCFV6SJCFTM5YHPVVEGFCFBBH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4KCFV6SJCFTM5YHPVVEGFCFBBH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4KCFV6SJCFTM5YHPVVEGFCFBBH/action/storage_attestation","attest_author":"https://pith.science/pith/4KCFV6SJCFTM5YHPVVEGFCFBBH/action/author_attestation","sign_citation":"https://pith.science/pith/4KCFV6SJCFTM5YHPVVEGFCFBBH/action/citation_signature","submit_replication":"https://pith.science/pith/4KCFV6SJCFTM5YHPVVEGFCFBBH/action/replication_record"}},"created_at":"2026-06-11T01:09:18.313590+00:00","updated_at":"2026-06-11T01:09:18.313590+00:00"}