{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:SR2FM36TDLTJVM2A4IGZ6FA5Q7","short_pith_number":"pith:SR2FM36T","schema_version":"1.0","canonical_sha256":"9474566fd31ae69ab340e20d9f141d87db035b50024118c433e97b8b8c3b118c","source":{"kind":"arxiv","id":"1905.02374","version":1},"attestation_state":"computed","paper":{"title":"Estimate Sequences for Variance-Reduced Stochastic Composite Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.OC"],"primary_cat":"stat.ML","authors_text":"Andrei Kulunchakov (Thoth), Julien Mairal (Thoth)","submitted_at":"2019-05-07T06:41:24Z","abstract_excerpt":"In this paper, we propose a unified view of gradient-based algorithms for stochastic convex composite optimization by extending the concept of estimate sequence introduced by Nesterov. This point of view covers the stochastic gradient descent method, variants of the approaches SAGA, SVRG, and has several advantages: (i) we provide a generic proof of convergence for the aforementioned methods; (ii) we show that this SVRG variant is adaptive to strong convexity; (iii) we naturally obtain new algorithms with the same guarantees; (iv) we derive generic strategies to make these algorithms robust to"},"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":"1905.02374","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-05-07T06:41:24Z","cross_cats_sorted":["cs.LG","math.OC"],"title_canon_sha256":"994f61cb4fc8e2bb62913ab7bf492a9a6166c947a6e94185d61cffeb21303340","abstract_canon_sha256":"77d2800d73f5de487bad97f7f256a9e09bc8dc298615ff2ed7bbd2afafade1b8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:51.647258Z","signature_b64":"mZZc6eAIisoNAH4LtrRhgP71p+YcnbSES+P+7xtLeQlqrQHNAJW0kBmPXRm08PUx+anwn9hD/so6UTs40TacBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9474566fd31ae69ab340e20d9f141d87db035b50024118c433e97b8b8c3b118c","last_reissued_at":"2026-05-17T23:46:51.646570Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:51.646570Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Estimate Sequences for Variance-Reduced Stochastic Composite Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.OC"],"primary_cat":"stat.ML","authors_text":"Andrei Kulunchakov (Thoth), Julien Mairal (Thoth)","submitted_at":"2019-05-07T06:41:24Z","abstract_excerpt":"In this paper, we propose a unified view of gradient-based algorithms for stochastic convex composite optimization by extending the concept of estimate sequence introduced by Nesterov. This point of view covers the stochastic gradient descent method, variants of the approaches SAGA, SVRG, and has several advantages: (i) we provide a generic proof of convergence for the aforementioned methods; (ii) we show that this SVRG variant is adaptive to strong convexity; (iii) we naturally obtain new algorithms with the same guarantees; (iv) we derive generic strategies to make these algorithms robust to"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.02374","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":"1905.02374","created_at":"2026-05-17T23:46:51.646672+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.02374v1","created_at":"2026-05-17T23:46:51.646672+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.02374","created_at":"2026-05-17T23:46:51.646672+00:00"},{"alias_kind":"pith_short_12","alias_value":"SR2FM36TDLTJ","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_16","alias_value":"SR2FM36TDLTJVM2A","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_8","alias_value":"SR2FM36T","created_at":"2026-05-18T12:33:27.125529+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/SR2FM36TDLTJVM2A4IGZ6FA5Q7","json":"https://pith.science/pith/SR2FM36TDLTJVM2A4IGZ6FA5Q7.json","graph_json":"https://pith.science/api/pith-number/SR2FM36TDLTJVM2A4IGZ6FA5Q7/graph.json","events_json":"https://pith.science/api/pith-number/SR2FM36TDLTJVM2A4IGZ6FA5Q7/events.json","paper":"https://pith.science/paper/SR2FM36T"},"agent_actions":{"view_html":"https://pith.science/pith/SR2FM36TDLTJVM2A4IGZ6FA5Q7","download_json":"https://pith.science/pith/SR2FM36TDLTJVM2A4IGZ6FA5Q7.json","view_paper":"https://pith.science/paper/SR2FM36T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.02374&json=true","fetch_graph":"https://pith.science/api/pith-number/SR2FM36TDLTJVM2A4IGZ6FA5Q7/graph.json","fetch_events":"https://pith.science/api/pith-number/SR2FM36TDLTJVM2A4IGZ6FA5Q7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SR2FM36TDLTJVM2A4IGZ6FA5Q7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SR2FM36TDLTJVM2A4IGZ6FA5Q7/action/storage_attestation","attest_author":"https://pith.science/pith/SR2FM36TDLTJVM2A4IGZ6FA5Q7/action/author_attestation","sign_citation":"https://pith.science/pith/SR2FM36TDLTJVM2A4IGZ6FA5Q7/action/citation_signature","submit_replication":"https://pith.science/pith/SR2FM36TDLTJVM2A4IGZ6FA5Q7/action/replication_record"}},"created_at":"2026-05-17T23:46:51.646672+00:00","updated_at":"2026-05-17T23:46:51.646672+00:00"}