{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ZYHDTOIPHZ5K2WZWLL3HO2FDE3","short_pith_number":"pith:ZYHDTOIP","schema_version":"1.0","canonical_sha256":"ce0e39b90f3e7aad5b365af67768a326c93c59c6e970b7c053833f07c369b628","source":{"kind":"arxiv","id":"2505.15303","version":1},"attestation_state":"computed","paper":{"title":"Laplace Sample Information: Data Informativeness Through a Bayesian Lens","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.IT","math.IT"],"primary_cat":"cs.LG","authors_text":"Daniel Rueckert, Georgios Kaissis, Johannes Kaiser, Kristian Schwethelm","submitted_at":"2025-05-21T09:34:27Z","abstract_excerpt":"Accurately estimating the informativeness of individual samples in a dataset is an important objective in deep learning, as it can guide sample selection, which can improve model efficiency and accuracy by removing redundant or potentially harmful samples. We propose Laplace Sample Information (LSI) measure of sample informativeness grounded in information theory widely applicable across model architectures and learning settings. LSI leverages a Bayesian approximation to the weight posterior and the KL divergence to measure the change in the parameter distribution induced by a sample of intere"},"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":"2505.15303","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-21T09:34:27Z","cross_cats_sorted":["cs.AI","cs.IT","math.IT"],"title_canon_sha256":"b7bdb10fb974307659bc7ba60435c11f30db20ba70d9bad9123fb721309ddda0","abstract_canon_sha256":"d5abbcb7868d9b1cc6221d2683f0744847a20d5222c59b552aeda4299064eacb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:06:43.464189Z","signature_b64":"dJ44+tA+BYa3M9lfHE9OIcyKm7Pk7Zicu0SJIXUGw3qkgtCwq+kyTsYwhDmcz6p1XvWhTBHpthcLD26v7PvoBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ce0e39b90f3e7aad5b365af67768a326c93c59c6e970b7c053833f07c369b628","last_reissued_at":"2026-07-05T11:06:43.463750Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:06:43.463750Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Laplace Sample Information: Data Informativeness Through a Bayesian Lens","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.IT","math.IT"],"primary_cat":"cs.LG","authors_text":"Daniel Rueckert, Georgios Kaissis, Johannes Kaiser, Kristian Schwethelm","submitted_at":"2025-05-21T09:34:27Z","abstract_excerpt":"Accurately estimating the informativeness of individual samples in a dataset is an important objective in deep learning, as it can guide sample selection, which can improve model efficiency and accuracy by removing redundant or potentially harmful samples. We propose Laplace Sample Information (LSI) measure of sample informativeness grounded in information theory widely applicable across model architectures and learning settings. LSI leverages a Bayesian approximation to the weight posterior and the KL divergence to measure the change in the parameter distribution induced by a sample of intere"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.15303","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2505.15303/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":"2505.15303","created_at":"2026-07-05T11:06:43.463806+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.15303v1","created_at":"2026-07-05T11:06:43.463806+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.15303","created_at":"2026-07-05T11:06:43.463806+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZYHDTOIPHZ5K","created_at":"2026-07-05T11:06:43.463806+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZYHDTOIPHZ5K2WZW","created_at":"2026-07-05T11:06:43.463806+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZYHDTOIP","created_at":"2026-07-05T11:06:43.463806+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/ZYHDTOIPHZ5K2WZWLL3HO2FDE3","json":"https://pith.science/pith/ZYHDTOIPHZ5K2WZWLL3HO2FDE3.json","graph_json":"https://pith.science/api/pith-number/ZYHDTOIPHZ5K2WZWLL3HO2FDE3/graph.json","events_json":"https://pith.science/api/pith-number/ZYHDTOIPHZ5K2WZWLL3HO2FDE3/events.json","paper":"https://pith.science/paper/ZYHDTOIP"},"agent_actions":{"view_html":"https://pith.science/pith/ZYHDTOIPHZ5K2WZWLL3HO2FDE3","download_json":"https://pith.science/pith/ZYHDTOIPHZ5K2WZWLL3HO2FDE3.json","view_paper":"https://pith.science/paper/ZYHDTOIP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.15303&json=true","fetch_graph":"https://pith.science/api/pith-number/ZYHDTOIPHZ5K2WZWLL3HO2FDE3/graph.json","fetch_events":"https://pith.science/api/pith-number/ZYHDTOIPHZ5K2WZWLL3HO2FDE3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZYHDTOIPHZ5K2WZWLL3HO2FDE3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZYHDTOIPHZ5K2WZWLL3HO2FDE3/action/storage_attestation","attest_author":"https://pith.science/pith/ZYHDTOIPHZ5K2WZWLL3HO2FDE3/action/author_attestation","sign_citation":"https://pith.science/pith/ZYHDTOIPHZ5K2WZWLL3HO2FDE3/action/citation_signature","submit_replication":"https://pith.science/pith/ZYHDTOIPHZ5K2WZWLL3HO2FDE3/action/replication_record"}},"created_at":"2026-07-05T11:06:43.463806+00:00","updated_at":"2026-07-05T11:06:43.463806+00:00"}