{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:ZOHIRARTJ5VZOA6E3UW6R4DNF4","short_pith_number":"pith:ZOHIRART","schema_version":"1.0","canonical_sha256":"cb8e8882334f6b9703c4dd2de8f06d2f08c611f248df45143c791e6066468761","source":{"kind":"arxiv","id":"1909.10072","version":1},"attestation_state":"computed","paper":{"title":"A generalization of regularized dual averaging and its dynamics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Guang Cheng, Shih-Kang Chao","submitted_at":"2019-09-22T19:12:26Z","abstract_excerpt":"Excessive computational cost for learning large data and streaming data can be alleviated by using stochastic algorithms, such as stochastic gradient descent and its variants. Recent advances improve stochastic algorithms on convergence speed, adaptivity and structural awareness. However, distributional aspects of these new algorithms are poorly understood, especially for structured parameters. To develop statistical inference in this case, we propose a class of generalized regularized dual averaging (gRDA) algorithms with constant step size, which improves RDA (Xiao, 2010; Flammarion and Bach"},"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":"1909.10072","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-09-22T19:12:26Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4d604af8321d6d0854ab473e77043166fe161cf57104175e6ea59b7a283e714c","abstract_canon_sha256":"a64645c8371dce6904a75c55df5301d35704aaea12152a955a932939f818c543"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:06:17.319883Z","signature_b64":"qvL/J/3JOZntF2FLbgpB+IL7UE4pH/5fsa6L9SnOfiOoT2GNcv9cB+R6007d61XDMeS0riiet+y69d6Xs5+aCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cb8e8882334f6b9703c4dd2de8f06d2f08c611f248df45143c791e6066468761","last_reissued_at":"2026-07-05T00:06:17.319459Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:06:17.319459Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A generalization of regularized dual averaging and its dynamics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Guang Cheng, Shih-Kang Chao","submitted_at":"2019-09-22T19:12:26Z","abstract_excerpt":"Excessive computational cost for learning large data and streaming data can be alleviated by using stochastic algorithms, such as stochastic gradient descent and its variants. Recent advances improve stochastic algorithms on convergence speed, adaptivity and structural awareness. However, distributional aspects of these new algorithms are poorly understood, especially for structured parameters. To develop statistical inference in this case, we propose a class of generalized regularized dual averaging (gRDA) algorithms with constant step size, which improves RDA (Xiao, 2010; Flammarion and Bach"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.10072","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/1909.10072/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":"1909.10072","created_at":"2026-07-05T00:06:17.319521+00:00"},{"alias_kind":"arxiv_version","alias_value":"1909.10072v1","created_at":"2026-07-05T00:06:17.319521+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.10072","created_at":"2026-07-05T00:06:17.319521+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZOHIRARTJ5VZ","created_at":"2026-07-05T00:06:17.319521+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZOHIRARTJ5VZOA6E","created_at":"2026-07-05T00:06:17.319521+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZOHIRART","created_at":"2026-07-05T00:06:17.319521+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/ZOHIRARTJ5VZOA6E3UW6R4DNF4","json":"https://pith.science/pith/ZOHIRARTJ5VZOA6E3UW6R4DNF4.json","graph_json":"https://pith.science/api/pith-number/ZOHIRARTJ5VZOA6E3UW6R4DNF4/graph.json","events_json":"https://pith.science/api/pith-number/ZOHIRARTJ5VZOA6E3UW6R4DNF4/events.json","paper":"https://pith.science/paper/ZOHIRART"},"agent_actions":{"view_html":"https://pith.science/pith/ZOHIRARTJ5VZOA6E3UW6R4DNF4","download_json":"https://pith.science/pith/ZOHIRARTJ5VZOA6E3UW6R4DNF4.json","view_paper":"https://pith.science/paper/ZOHIRART","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1909.10072&json=true","fetch_graph":"https://pith.science/api/pith-number/ZOHIRARTJ5VZOA6E3UW6R4DNF4/graph.json","fetch_events":"https://pith.science/api/pith-number/ZOHIRARTJ5VZOA6E3UW6R4DNF4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZOHIRARTJ5VZOA6E3UW6R4DNF4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZOHIRARTJ5VZOA6E3UW6R4DNF4/action/storage_attestation","attest_author":"https://pith.science/pith/ZOHIRARTJ5VZOA6E3UW6R4DNF4/action/author_attestation","sign_citation":"https://pith.science/pith/ZOHIRARTJ5VZOA6E3UW6R4DNF4/action/citation_signature","submit_replication":"https://pith.science/pith/ZOHIRARTJ5VZOA6E3UW6R4DNF4/action/replication_record"}},"created_at":"2026-07-05T00:06:17.319521+00:00","updated_at":"2026-07-05T00:06:17.319521+00:00"}