{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:OYYRTMEHLT3G6MDWIPKZRQ55ZG","short_pith_number":"pith:OYYRTMEH","schema_version":"1.0","canonical_sha256":"763119b0875cf66f307643d598c3bdc9925b5e12e41c8820459494926cc11bd1","source":{"kind":"arxiv","id":"1807.04709","version":3},"attestation_state":"computed","paper":{"title":"Inferring Multidimensional Rates of Aging from Cross-Sectional Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Daphne Koller, Emma Pierson, Jure Leskovec, Nicholas Eriksson, Pang Wei Koh, Percy Liang, Tatsunori Hashimoto","submitted_at":"2018-07-12T16:27:40Z","abstract_excerpt":"Modeling how individuals evolve over time is a fundamental problem in the natural and social sciences. However, existing datasets are often cross-sectional with each individual observed only once, making it impossible to apply traditional time-series methods. Motivated by the study of human aging, we present an interpretable latent-variable model that learns temporal dynamics from cross-sectional data. Our model represents each individual's features over time as a nonlinear function of a low-dimensional, linearly-evolving latent state. We prove that when this nonlinear function is constrained "},"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":"1807.04709","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-12T16:27:40Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"768e6042e13b87b13560093375641966799af19b6489aac321d66412aadd6364","abstract_canon_sha256":"f094bfcb7f6aceedec6c4022720329b76a7ada49ab46ec1fd214b7c0968531b7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:07.312226Z","signature_b64":"QjYFcfgi0ZIJ/cIcq0IyC/UJfOuZyujTsZhWZDDva22Utv/ibOkzIl09cuncvAVNzw8V4MJ0z8JAhZOsBL1wBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"763119b0875cf66f307643d598c3bdc9925b5e12e41c8820459494926cc11bd1","last_reissued_at":"2026-05-17T23:52:07.311769Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:07.311769Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Inferring Multidimensional Rates of Aging from Cross-Sectional Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Daphne Koller, Emma Pierson, Jure Leskovec, Nicholas Eriksson, Pang Wei Koh, Percy Liang, Tatsunori Hashimoto","submitted_at":"2018-07-12T16:27:40Z","abstract_excerpt":"Modeling how individuals evolve over time is a fundamental problem in the natural and social sciences. However, existing datasets are often cross-sectional with each individual observed only once, making it impossible to apply traditional time-series methods. Motivated by the study of human aging, we present an interpretable latent-variable model that learns temporal dynamics from cross-sectional data. Our model represents each individual's features over time as a nonlinear function of a low-dimensional, linearly-evolving latent state. We prove that when this nonlinear function is constrained "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.04709","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":""},"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":"1807.04709","created_at":"2026-05-17T23:52:07.311838+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.04709v3","created_at":"2026-05-17T23:52:07.311838+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.04709","created_at":"2026-05-17T23:52:07.311838+00:00"},{"alias_kind":"pith_short_12","alias_value":"OYYRTMEHLT3G","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_16","alias_value":"OYYRTMEHLT3G6MDW","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_8","alias_value":"OYYRTMEH","created_at":"2026-05-18T12:32:43.782077+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/OYYRTMEHLT3G6MDWIPKZRQ55ZG","json":"https://pith.science/pith/OYYRTMEHLT3G6MDWIPKZRQ55ZG.json","graph_json":"https://pith.science/api/pith-number/OYYRTMEHLT3G6MDWIPKZRQ55ZG/graph.json","events_json":"https://pith.science/api/pith-number/OYYRTMEHLT3G6MDWIPKZRQ55ZG/events.json","paper":"https://pith.science/paper/OYYRTMEH"},"agent_actions":{"view_html":"https://pith.science/pith/OYYRTMEHLT3G6MDWIPKZRQ55ZG","download_json":"https://pith.science/pith/OYYRTMEHLT3G6MDWIPKZRQ55ZG.json","view_paper":"https://pith.science/paper/OYYRTMEH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.04709&json=true","fetch_graph":"https://pith.science/api/pith-number/OYYRTMEHLT3G6MDWIPKZRQ55ZG/graph.json","fetch_events":"https://pith.science/api/pith-number/OYYRTMEHLT3G6MDWIPKZRQ55ZG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OYYRTMEHLT3G6MDWIPKZRQ55ZG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OYYRTMEHLT3G6MDWIPKZRQ55ZG/action/storage_attestation","attest_author":"https://pith.science/pith/OYYRTMEHLT3G6MDWIPKZRQ55ZG/action/author_attestation","sign_citation":"https://pith.science/pith/OYYRTMEHLT3G6MDWIPKZRQ55ZG/action/citation_signature","submit_replication":"https://pith.science/pith/OYYRTMEHLT3G6MDWIPKZRQ55ZG/action/replication_record"}},"created_at":"2026-05-17T23:52:07.311838+00:00","updated_at":"2026-05-17T23:52:07.311838+00:00"}