{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2011:7OB4K2ZSWRRCJUCFEG5IEGVL3K","short_pith_number":"pith:7OB4K2ZS","schema_version":"1.0","canonical_sha256":"fb83c56b32b46224d04521ba821aabdab37cb21be5cafdd56d7a60536302cf6c","source":{"kind":"arxiv","id":"1106.1216","version":2},"attestation_state":"computed","paper":{"title":"Using More Data to Speed-up Training Time","license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Eran Tromer, Ohad Shamir, Shai Shalev-Shwartz","submitted_at":"2011-06-06T23:55:00Z","abstract_excerpt":"In many recent applications, data is plentiful. By now, we have a rather clear understanding of how more data can be used to improve the accuracy of learning algorithms. Recently, there has been a growing interest in understanding how more data can be leveraged to reduce the required training runtime. In this paper, we study the runtime of learning as a function of the number of available training examples, and underscore the main high-level techniques. We provide some initial positive results showing that the runtime can decrease exponentially while only requiring a polynomial growth of the n"},"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":"1106.1216","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"cs.LG","submitted_at":"2011-06-06T23:55:00Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"f3b2af8038c5ac009584685777f0a154739db2eac8fb5dc43749b15525807280","abstract_canon_sha256":"70dd5bc8b3dcecc270654d8d45c267a6fcd4f8351dd1b32ac23b3c74280120fd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:19:55.592630Z","signature_b64":"kqoyjRwib185EtDJjALQvD+nBD8nqiXsghLm+w7CqNsLGCsP/lgFKcfC06C2v0EXq1IDqcFn5+mI1I0i7aPbDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fb83c56b32b46224d04521ba821aabdab37cb21be5cafdd56d7a60536302cf6c","last_reissued_at":"2026-05-18T04:19:55.591945Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:19:55.591945Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Using More Data to Speed-up Training Time","license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Eran Tromer, Ohad Shamir, Shai Shalev-Shwartz","submitted_at":"2011-06-06T23:55:00Z","abstract_excerpt":"In many recent applications, data is plentiful. By now, we have a rather clear understanding of how more data can be used to improve the accuracy of learning algorithms. Recently, there has been a growing interest in understanding how more data can be leveraged to reduce the required training runtime. In this paper, we study the runtime of learning as a function of the number of available training examples, and underscore the main high-level techniques. We provide some initial positive results showing that the runtime can decrease exponentially while only requiring a polynomial growth of the n"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1106.1216","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1106.1216","created_at":"2026-05-18T04:19:55.592037+00:00"},{"alias_kind":"arxiv_version","alias_value":"1106.1216v2","created_at":"2026-05-18T04:19:55.592037+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1106.1216","created_at":"2026-05-18T04:19:55.592037+00:00"},{"alias_kind":"pith_short_12","alias_value":"7OB4K2ZSWRRC","created_at":"2026-05-18T12:26:22.705136+00:00"},{"alias_kind":"pith_short_16","alias_value":"7OB4K2ZSWRRCJUCF","created_at":"2026-05-18T12:26:22.705136+00:00"},{"alias_kind":"pith_short_8","alias_value":"7OB4K2ZS","created_at":"2026-05-18T12:26:22.705136+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/7OB4K2ZSWRRCJUCFEG5IEGVL3K","json":"https://pith.science/pith/7OB4K2ZSWRRCJUCFEG5IEGVL3K.json","graph_json":"https://pith.science/api/pith-number/7OB4K2ZSWRRCJUCFEG5IEGVL3K/graph.json","events_json":"https://pith.science/api/pith-number/7OB4K2ZSWRRCJUCFEG5IEGVL3K/events.json","paper":"https://pith.science/paper/7OB4K2ZS"},"agent_actions":{"view_html":"https://pith.science/pith/7OB4K2ZSWRRCJUCFEG5IEGVL3K","download_json":"https://pith.science/pith/7OB4K2ZSWRRCJUCFEG5IEGVL3K.json","view_paper":"https://pith.science/paper/7OB4K2ZS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1106.1216&json=true","fetch_graph":"https://pith.science/api/pith-number/7OB4K2ZSWRRCJUCFEG5IEGVL3K/graph.json","fetch_events":"https://pith.science/api/pith-number/7OB4K2ZSWRRCJUCFEG5IEGVL3K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7OB4K2ZSWRRCJUCFEG5IEGVL3K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7OB4K2ZSWRRCJUCFEG5IEGVL3K/action/storage_attestation","attest_author":"https://pith.science/pith/7OB4K2ZSWRRCJUCFEG5IEGVL3K/action/author_attestation","sign_citation":"https://pith.science/pith/7OB4K2ZSWRRCJUCFEG5IEGVL3K/action/citation_signature","submit_replication":"https://pith.science/pith/7OB4K2ZSWRRCJUCFEG5IEGVL3K/action/replication_record"}},"created_at":"2026-05-18T04:19:55.592037+00:00","updated_at":"2026-05-18T04:19:55.592037+00:00"}