{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:IITVCRQVG2MS5SDPU5ZM5U74Y7","short_pith_number":"pith:IITVCRQV","canonical_record":{"source":{"id":"2007.11022","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-21T18:15:08Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"3ed6c11e729a31607b763f237799c9e9aceb3b37b67eaff46ed373e3eeeadf16","abstract_canon_sha256":"f9b5888affb907efafeade231f7a9558d1d5acf1b50fd3fe72be4ab43c39d7ce"},"schema_version":"1.0"},"canonical_sha256":"422751461536992ec86fa772ced3fcc7f2cf437d82a8d1a8bcd42b75e3686ff6","source":{"kind":"arxiv","id":"2007.11022","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.11022","created_at":"2026-07-05T01:21:21Z"},{"alias_kind":"arxiv_version","alias_value":"2007.11022v1","created_at":"2026-07-05T01:21:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.11022","created_at":"2026-07-05T01:21:21Z"},{"alias_kind":"pith_short_12","alias_value":"IITVCRQVG2MS","created_at":"2026-07-05T01:21:21Z"},{"alias_kind":"pith_short_16","alias_value":"IITVCRQVG2MS5SDP","created_at":"2026-07-05T01:21:21Z"},{"alias_kind":"pith_short_8","alias_value":"IITVCRQV","created_at":"2026-07-05T01:21:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:IITVCRQVG2MS5SDPU5ZM5U74Y7","target":"record","payload":{"canonical_record":{"source":{"id":"2007.11022","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-21T18:15:08Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"3ed6c11e729a31607b763f237799c9e9aceb3b37b67eaff46ed373e3eeeadf16","abstract_canon_sha256":"f9b5888affb907efafeade231f7a9558d1d5acf1b50fd3fe72be4ab43c39d7ce"},"schema_version":"1.0"},"canonical_sha256":"422751461536992ec86fa772ced3fcc7f2cf437d82a8d1a8bcd42b75e3686ff6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:21:21.775509Z","signature_b64":"g2iFpnumlPIWWmsDgPiObFXHFCpP+9xTvJ1uZ89GpK0uKnoWV6RmG4gnGLpvH4Fv5E58pk5uCzrV53pi75U9BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"422751461536992ec86fa772ced3fcc7f2cf437d82a8d1a8bcd42b75e3686ff6","last_reissued_at":"2026-07-05T01:21:21.775022Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:21:21.775022Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2007.11022","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":"2026-07-05T01:21:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oeOzD79kQJRxlbn0zSiVus4K0OsQpWvsBXMz+afmGdTJWAb97/9VSQ3dcSzzvNeajC/DCL9G0pzObU/cG/x/DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T06:14:24.584343Z"},"content_sha256":"7d1dc09cf847b59910493adefde0c4a7cf21d43ed998ac9a92d0d48c63118a23","schema_version":"1.0","event_id":"sha256:7d1dc09cf847b59910493adefde0c4a7cf21d43ed998ac9a92d0d48c63118a23"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:IITVCRQVG2MS5SDPU5ZM5U74Y7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Gradient-based Bilevel Optimization Approach for Tuning Hyperparameters in Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ankur Sinha, Raja Mohanty, Tanmay Khandait","submitted_at":"2020-07-21T18:15:08Z","abstract_excerpt":"Hyperparameter tuning is an active area of research in machine learning, where the aim is to identify the optimal hyperparameters that provide the best performance on the validation set. Hyperparameter tuning is often achieved using naive techniques, such as random search and grid search. However, most of these methods seldom lead to an optimal set of hyperparameters and often get very expensive. In this paper, we propose a bilevel solution method for solving the hyperparameter optimization problem that does not suffer from the drawbacks of the earlier studies. The proposed method is general a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.11022","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/2007.11022/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":"2026-07-05T01:21:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"idOEt2EzZecsc62Z1v4jbhEsdD77Te+aCcVVZ+Q1zrNzUTBTpRACi3bVzDWYJkXZxXQI9uY2GtE5f8uDLTMPCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T06:14:24.584848Z"},"content_sha256":"af57ba82a0cade4c7881e31dd5d24499f814de94a010b6e024b6c063b2737a92","schema_version":"1.0","event_id":"sha256:af57ba82a0cade4c7881e31dd5d24499f814de94a010b6e024b6c063b2737a92"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IITVCRQVG2MS5SDPU5ZM5U74Y7/bundle.json","state_url":"https://pith.science/pith/IITVCRQVG2MS5SDPU5ZM5U74Y7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IITVCRQVG2MS5SDPU5ZM5U74Y7/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":"2026-08-15T06:14:24Z","links":{"resolver":"https://pith.science/pith/IITVCRQVG2MS5SDPU5ZM5U74Y7","bundle":"https://pith.science/pith/IITVCRQVG2MS5SDPU5ZM5U74Y7/bundle.json","state":"https://pith.science/pith/IITVCRQVG2MS5SDPU5ZM5U74Y7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IITVCRQVG2MS5SDPU5ZM5U74Y7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:IITVCRQVG2MS5SDPU5ZM5U74Y7","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":"f9b5888affb907efafeade231f7a9558d1d5acf1b50fd3fe72be4ab43c39d7ce","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-21T18:15:08Z","title_canon_sha256":"3ed6c11e729a31607b763f237799c9e9aceb3b37b67eaff46ed373e3eeeadf16"},"schema_version":"1.0","source":{"id":"2007.11022","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.11022","created_at":"2026-07-05T01:21:21Z"},{"alias_kind":"arxiv_version","alias_value":"2007.11022v1","created_at":"2026-07-05T01:21:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.11022","created_at":"2026-07-05T01:21:21Z"},{"alias_kind":"pith_short_12","alias_value":"IITVCRQVG2MS","created_at":"2026-07-05T01:21:21Z"},{"alias_kind":"pith_short_16","alias_value":"IITVCRQVG2MS5SDP","created_at":"2026-07-05T01:21:21Z"},{"alias_kind":"pith_short_8","alias_value":"IITVCRQV","created_at":"2026-07-05T01:21:21Z"}],"graph_snapshots":[{"event_id":"sha256:af57ba82a0cade4c7881e31dd5d24499f814de94a010b6e024b6c063b2737a92","target":"graph","created_at":"2026-07-05T01:21:21Z","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/2007.11022/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Hyperparameter tuning is an active area of research in machine learning, where the aim is to identify the optimal hyperparameters that provide the best performance on the validation set. Hyperparameter tuning is often achieved using naive techniques, such as random search and grid search. However, most of these methods seldom lead to an optimal set of hyperparameters and often get very expensive. In this paper, we propose a bilevel solution method for solving the hyperparameter optimization problem that does not suffer from the drawbacks of the earlier studies. The proposed method is general a","authors_text":"Ankur Sinha, Raja Mohanty, Tanmay Khandait","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-21T18:15:08Z","title":"A Gradient-based Bilevel Optimization Approach for Tuning Hyperparameters in Machine Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.11022","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":"sha256:7d1dc09cf847b59910493adefde0c4a7cf21d43ed998ac9a92d0d48c63118a23","target":"record","created_at":"2026-07-05T01:21:21Z","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":"f9b5888affb907efafeade231f7a9558d1d5acf1b50fd3fe72be4ab43c39d7ce","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-21T18:15:08Z","title_canon_sha256":"3ed6c11e729a31607b763f237799c9e9aceb3b37b67eaff46ed373e3eeeadf16"},"schema_version":"1.0","source":{"id":"2007.11022","kind":"arxiv","version":1}},"canonical_sha256":"422751461536992ec86fa772ced3fcc7f2cf437d82a8d1a8bcd42b75e3686ff6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"422751461536992ec86fa772ced3fcc7f2cf437d82a8d1a8bcd42b75e3686ff6","first_computed_at":"2026-07-05T01:21:21.775022Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:21:21.775022Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"g2iFpnumlPIWWmsDgPiObFXHFCpP+9xTvJ1uZ89GpK0uKnoWV6RmG4gnGLpvH4Fv5E58pk5uCzrV53pi75U9BA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:21:21.775509Z","signed_message":"canonical_sha256_bytes"},"source_id":"2007.11022","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7d1dc09cf847b59910493adefde0c4a7cf21d43ed998ac9a92d0d48c63118a23","sha256:af57ba82a0cade4c7881e31dd5d24499f814de94a010b6e024b6c063b2737a92"],"state_sha256":"ac60874e5d138517a408de2b14851374f76c82ffa0feee7d8c618f201383bbff"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"feJkTIZLmd+Hlp1tYXYLket5xaZbE8ZsY5V2DWeP+xsiTm0BwWe7Lh2v2JLfaaH4j01fhhVpoktkx3ZbNvuIBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T06:14:24.588161Z","bundle_sha256":"d2057d1f14c4c0203c533f842a9e477a0737c3ec6e3f227c2aaa573fbff0f14f"}}