{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:E4W7LHOKZPHJDF5DK5NU4UXHDM","short_pith_number":"pith:E4W7LHOK","canonical_record":{"source":{"id":"2410.10417","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-14T12:10:06Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"3cce989edade6b7a5e90e799e21ec1f015484fa761859d4c4b678b6594f74881","abstract_canon_sha256":"de4c44a7ff7df2e6433eb77bddad3f69bcd95cf1463b39f161adb7ece2362e37"},"schema_version":"1.0"},"canonical_sha256":"272df59dcacbce9197a3575b4e52e71b399560f950b0d3c11afb2083945ab247","source":{"kind":"arxiv","id":"2410.10417","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.10417","created_at":"2026-07-05T09:20:15Z"},{"alias_kind":"arxiv_version","alias_value":"2410.10417v1","created_at":"2026-07-05T09:20:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.10417","created_at":"2026-07-05T09:20:15Z"},{"alias_kind":"pith_short_12","alias_value":"E4W7LHOKZPHJ","created_at":"2026-07-05T09:20:15Z"},{"alias_kind":"pith_short_16","alias_value":"E4W7LHOKZPHJDF5D","created_at":"2026-07-05T09:20:15Z"},{"alias_kind":"pith_short_8","alias_value":"E4W7LHOK","created_at":"2026-07-05T09:20:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:E4W7LHOKZPHJDF5DK5NU4UXHDM","target":"record","payload":{"canonical_record":{"source":{"id":"2410.10417","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-14T12:10:06Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"3cce989edade6b7a5e90e799e21ec1f015484fa761859d4c4b678b6594f74881","abstract_canon_sha256":"de4c44a7ff7df2e6433eb77bddad3f69bcd95cf1463b39f161adb7ece2362e37"},"schema_version":"1.0"},"canonical_sha256":"272df59dcacbce9197a3575b4e52e71b399560f950b0d3c11afb2083945ab247","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:20:15.923030Z","signature_b64":"gMo6j+lqWIAjyQ3euEgXuaRuRrpHZmLUMTUjZEJVo79Ats8ssYsWjRFwY22BAaPKSo2Megv7Ame2+R4IekViDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"272df59dcacbce9197a3575b4e52e71b399560f950b0d3c11afb2083945ab247","last_reissued_at":"2026-07-05T09:20:15.922621Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:20:15.922621Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.10417","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:20:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Em+j91kZeTimxttQ181zSgXWGTYzFvatwxpUDNaDvTGEB7HH6luOXe8dLpOjeaAaAGA5LceVbXmU04wd0jiGAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T10:04:27.953019Z"},"content_sha256":"0861c31345e3733999ff1ae9f011b67e438715acfb7a4b720506d753c0f39e14","schema_version":"1.0","event_id":"sha256:0861c31345e3733999ff1ae9f011b67e438715acfb7a4b720506d753c0f39e14"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:E4W7LHOKZPHJDF5DK5NU4UXHDM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Minyoung Kim, Timothy M. Hospedales","submitted_at":"2024-10-14T12:10:06Z","abstract_excerpt":"We tackle the general differentiable meta learning problem that is ubiquitous in modern deep learning, including hyperparameter optimization, loss function learning, few-shot learning, invariance learning and more. These problems are often formalized as Bi-Level optimizations (BLO). We introduce a novel perspective by turning a given BLO problem into a stochastic optimization, where the inner loss function becomes a smooth probability distribution, and the outer loss becomes an expected loss over the inner distribution. To solve this stochastic optimization, we adopt Stochastic Gradient Langev"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.10417","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/2410.10417/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":"2026-07-05T09:20:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2J1gq3pFJEGGWenAUilPsTy/r2uK8HXaDZBk1BJbLbz1Eqb4WfZ9OafX5kGcn+Rm1MrQTivqZj6r3OZadLwdAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T10:04:27.953872Z"},"content_sha256":"84a475e0bb1f1241911a850253867fb1cef278513707724849b0235cd6600cd0","schema_version":"1.0","event_id":"sha256:84a475e0bb1f1241911a850253867fb1cef278513707724849b0235cd6600cd0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/E4W7LHOKZPHJDF5DK5NU4UXHDM/bundle.json","state_url":"https://pith.science/pith/E4W7LHOKZPHJDF5DK5NU4UXHDM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/E4W7LHOKZPHJDF5DK5NU4UXHDM/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":"2026-08-04T10:04:27Z","links":{"resolver":"https://pith.science/pith/E4W7LHOKZPHJDF5DK5NU4UXHDM","bundle":"https://pith.science/pith/E4W7LHOKZPHJDF5DK5NU4UXHDM/bundle.json","state":"https://pith.science/pith/E4W7LHOKZPHJDF5DK5NU4UXHDM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/E4W7LHOKZPHJDF5DK5NU4UXHDM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:E4W7LHOKZPHJDF5DK5NU4UXHDM","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":"de4c44a7ff7df2e6433eb77bddad3f69bcd95cf1463b39f161adb7ece2362e37","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-14T12:10:06Z","title_canon_sha256":"3cce989edade6b7a5e90e799e21ec1f015484fa761859d4c4b678b6594f74881"},"schema_version":"1.0","source":{"id":"2410.10417","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.10417","created_at":"2026-07-05T09:20:15Z"},{"alias_kind":"arxiv_version","alias_value":"2410.10417v1","created_at":"2026-07-05T09:20:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.10417","created_at":"2026-07-05T09:20:15Z"},{"alias_kind":"pith_short_12","alias_value":"E4W7LHOKZPHJ","created_at":"2026-07-05T09:20:15Z"},{"alias_kind":"pith_short_16","alias_value":"E4W7LHOKZPHJDF5D","created_at":"2026-07-05T09:20:15Z"},{"alias_kind":"pith_short_8","alias_value":"E4W7LHOK","created_at":"2026-07-05T09:20:15Z"}],"graph_snapshots":[{"event_id":"sha256:84a475e0bb1f1241911a850253867fb1cef278513707724849b0235cd6600cd0","target":"graph","created_at":"2026-07-05T09:20:15Z","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/2410.10417/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We tackle the general differentiable meta learning problem that is ubiquitous in modern deep learning, including hyperparameter optimization, loss function learning, few-shot learning, invariance learning and more. These problems are often formalized as Bi-Level optimizations (BLO). We introduce a novel perspective by turning a given BLO problem into a stochastic optimization, where the inner loss function becomes a smooth probability distribution, and the outer loss becomes an expected loss over the inner distribution. To solve this stochastic optimization, we adopt Stochastic Gradient Langev","authors_text":"Minyoung Kim, Timothy M. Hospedales","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-14T12:10:06Z","title":"A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.10417","kind":"arxiv","version":1},"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":"sha256:0861c31345e3733999ff1ae9f011b67e438715acfb7a4b720506d753c0f39e14","target":"record","created_at":"2026-07-05T09:20:15Z","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":"de4c44a7ff7df2e6433eb77bddad3f69bcd95cf1463b39f161adb7ece2362e37","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-14T12:10:06Z","title_canon_sha256":"3cce989edade6b7a5e90e799e21ec1f015484fa761859d4c4b678b6594f74881"},"schema_version":"1.0","source":{"id":"2410.10417","kind":"arxiv","version":1}},"canonical_sha256":"272df59dcacbce9197a3575b4e52e71b399560f950b0d3c11afb2083945ab247","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"272df59dcacbce9197a3575b4e52e71b399560f950b0d3c11afb2083945ab247","first_computed_at":"2026-07-05T09:20:15.922621Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:20:15.922621Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gMo6j+lqWIAjyQ3euEgXuaRuRrpHZmLUMTUjZEJVo79Ats8ssYsWjRFwY22BAaPKSo2Megv7Ame2+R4IekViDA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:20:15.923030Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.10417","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0861c31345e3733999ff1ae9f011b67e438715acfb7a4b720506d753c0f39e14","sha256:84a475e0bb1f1241911a850253867fb1cef278513707724849b0235cd6600cd0"],"state_sha256":"3d7b5d60b947f38252b57a370e084ac63b0ef52e2abdeb227c4d6c3f14dafef4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RJQ+QJuOACwEDBQ0Mw1M42g6Pod0OKN+lb1Duqz5jX67WdjiuNB05PqyHfJpXqKFx9C0hxrtda0CrkRA4LRiDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T10:04:27.959750Z","bundle_sha256":"42d022cf019c0bf4ebcc549eb4e3619fca35adc446932309ac91d93495e24b71"}}