{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:IXJRAX7GEUJETA5FAD6B4CJD35","short_pith_number":"pith:IXJRAX7G","canonical_record":{"source":{"id":"2102.04671","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2021-02-09T06:35:30Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"319301e238226f619c1a490cc9eabae5b72e06cf456323fa398caaa2ec656ce0","abstract_canon_sha256":"576a31ca32638fa69b9ad38f2d343cf4e49307a59dc34e46fde033bff31d0d38"},"schema_version":"1.0"},"canonical_sha256":"45d3105fe625124983a500fc1e0923df41aa576f7b925b65ef241715ecb638fc","source":{"kind":"arxiv","id":"2102.04671","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2102.04671","created_at":"2026-07-05T04:10:11Z"},{"alias_kind":"arxiv_version","alias_value":"2102.04671v4","created_at":"2026-07-05T04:10:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.04671","created_at":"2026-07-05T04:10:11Z"},{"alias_kind":"pith_short_12","alias_value":"IXJRAX7GEUJE","created_at":"2026-07-05T04:10:11Z"},{"alias_kind":"pith_short_16","alias_value":"IXJRAX7GEUJETA5F","created_at":"2026-07-05T04:10:11Z"},{"alias_kind":"pith_short_8","alias_value":"IXJRAX7G","created_at":"2026-07-05T04:10:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:IXJRAX7GEUJETA5FAD6B4CJD35","target":"record","payload":{"canonical_record":{"source":{"id":"2102.04671","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2021-02-09T06:35:30Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"319301e238226f619c1a490cc9eabae5b72e06cf456323fa398caaa2ec656ce0","abstract_canon_sha256":"576a31ca32638fa69b9ad38f2d343cf4e49307a59dc34e46fde033bff31d0d38"},"schema_version":"1.0"},"canonical_sha256":"45d3105fe625124983a500fc1e0923df41aa576f7b925b65ef241715ecb638fc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:10:11.755750Z","signature_b64":"/g7oFcsZk2B1UGiVd96qTgzEFFWSr/gMV0tp14RAVPn1LxYR9r8LjVJKuW2AiEbjzDcggtbKJW5kALJWgFknCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"45d3105fe625124983a500fc1e0923df41aa576f7b925b65ef241715ecb638fc","last_reissued_at":"2026-07-05T04:10:11.755158Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:10:11.755158Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2102.04671","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:10:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"200LamGDHOhRD0g9HhKxdhS9iszv7dm+nghxf2Is1BzB/LvaSkb5coyq3M+pUAp8c5+pBzC6RpwZq1Sfkwm7Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T16:02:11.844777Z"},"content_sha256":"00cb24a52e58ac5c4ab7ed5a57d27d9c43bea9d3c4061b1199b27d7d2f3cb999","schema_version":"1.0","event_id":"sha256:00cb24a52e58ac5c4ab7ed5a57d27d9c43bea9d3c4061b1199b27d7d2f3cb999"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:IXJRAX7GEUJETA5FAD6B4CJD35","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Single-Timescale Method for Stochastic Bilevel Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"math.OC","authors_text":"Quan Xiao, Tianyi Chen, Wotao Yin, Yuejiao Sun","submitted_at":"2021-02-09T06:35:30Z","abstract_excerpt":"Stochastic bilevel optimization generalizes the classic stochastic optimization from the minimization of a single objective to the minimization of an objective function that depends the solution of another optimization problem. Recently, stochastic bilevel optimization is regaining popularity in emerging machine learning applications such as hyper-parameter optimization and model-agnostic meta learning. To solve this class of stochastic optimization problems, existing methods require either double-loop or two-timescale updates, which are sometimes less efficient. This paper develops a new opti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.04671","kind":"arxiv","version":4},"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/2102.04671/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:10:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AePbq6w1PpUXSDlM9OZW8Zf9SElUUWxwHHLwVINHqUcBSv+r8i1kdXP11B/IGN0PQWhTe9Yit0gvYvzVXPbODQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T16:02:11.845152Z"},"content_sha256":"dbc5e5c3386a67ab9d92ed5f407d3943335b4c3b55f1669841b7489ecf4523c3","schema_version":"1.0","event_id":"sha256:dbc5e5c3386a67ab9d92ed5f407d3943335b4c3b55f1669841b7489ecf4523c3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IXJRAX7GEUJETA5FAD6B4CJD35/bundle.json","state_url":"https://pith.science/pith/IXJRAX7GEUJETA5FAD6B4CJD35/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IXJRAX7GEUJETA5FAD6B4CJD35/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-05T16:02:11Z","links":{"resolver":"https://pith.science/pith/IXJRAX7GEUJETA5FAD6B4CJD35","bundle":"https://pith.science/pith/IXJRAX7GEUJETA5FAD6B4CJD35/bundle.json","state":"https://pith.science/pith/IXJRAX7GEUJETA5FAD6B4CJD35/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IXJRAX7GEUJETA5FAD6B4CJD35/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:IXJRAX7GEUJETA5FAD6B4CJD35","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"576a31ca32638fa69b9ad38f2d343cf4e49307a59dc34e46fde033bff31d0d38","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2021-02-09T06:35:30Z","title_canon_sha256":"319301e238226f619c1a490cc9eabae5b72e06cf456323fa398caaa2ec656ce0"},"schema_version":"1.0","source":{"id":"2102.04671","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2102.04671","created_at":"2026-07-05T04:10:11Z"},{"alias_kind":"arxiv_version","alias_value":"2102.04671v4","created_at":"2026-07-05T04:10:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.04671","created_at":"2026-07-05T04:10:11Z"},{"alias_kind":"pith_short_12","alias_value":"IXJRAX7GEUJE","created_at":"2026-07-05T04:10:11Z"},{"alias_kind":"pith_short_16","alias_value":"IXJRAX7GEUJETA5F","created_at":"2026-07-05T04:10:11Z"},{"alias_kind":"pith_short_8","alias_value":"IXJRAX7G","created_at":"2026-07-05T04:10:11Z"}],"graph_snapshots":[{"event_id":"sha256:dbc5e5c3386a67ab9d92ed5f407d3943335b4c3b55f1669841b7489ecf4523c3","target":"graph","created_at":"2026-07-05T04:10:11Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2102.04671/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Stochastic bilevel optimization generalizes the classic stochastic optimization from the minimization of a single objective to the minimization of an objective function that depends the solution of another optimization problem. Recently, stochastic bilevel optimization is regaining popularity in emerging machine learning applications such as hyper-parameter optimization and model-agnostic meta learning. To solve this class of stochastic optimization problems, existing methods require either double-loop or two-timescale updates, which are sometimes less efficient. This paper develops a new opti","authors_text":"Quan Xiao, Tianyi Chen, Wotao Yin, Yuejiao Sun","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2021-02-09T06:35:30Z","title":"A Single-Timescale Method for Stochastic Bilevel Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.04671","kind":"arxiv","version":4},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:00cb24a52e58ac5c4ab7ed5a57d27d9c43bea9d3c4061b1199b27d7d2f3cb999","target":"record","created_at":"2026-07-05T04:10:11Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"576a31ca32638fa69b9ad38f2d343cf4e49307a59dc34e46fde033bff31d0d38","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2021-02-09T06:35:30Z","title_canon_sha256":"319301e238226f619c1a490cc9eabae5b72e06cf456323fa398caaa2ec656ce0"},"schema_version":"1.0","source":{"id":"2102.04671","kind":"arxiv","version":4}},"canonical_sha256":"45d3105fe625124983a500fc1e0923df41aa576f7b925b65ef241715ecb638fc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"45d3105fe625124983a500fc1e0923df41aa576f7b925b65ef241715ecb638fc","first_computed_at":"2026-07-05T04:10:11.755158Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:10:11.755158Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/g7oFcsZk2B1UGiVd96qTgzEFFWSr/gMV0tp14RAVPn1LxYR9r8LjVJKuW2AiEbjzDcggtbKJW5kALJWgFknCA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:10:11.755750Z","signed_message":"canonical_sha256_bytes"},"source_id":"2102.04671","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:00cb24a52e58ac5c4ab7ed5a57d27d9c43bea9d3c4061b1199b27d7d2f3cb999","sha256:dbc5e5c3386a67ab9d92ed5f407d3943335b4c3b55f1669841b7489ecf4523c3"],"state_sha256":"246c26621fded7d95453f7f20fb6545ca377a9d2dea971d3c4c56e1c8eb35573"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TASqrblKi/uA0+geGfYLUKtDv1DMN53lVSBFqAW6+8pQp1Eiv5IW6Me5yccFTv4CK/GStfzaO50mmbTN0m4gCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T16:02:11.847717Z","bundle_sha256":"241c944db28a56283d341e1b54937511bcedfe1d26a98db921580d4a476c592b"}}