{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:FE3YDDGYS3WJQTZEU4Y5DK4X3Y","short_pith_number":"pith:FE3YDDGY","canonical_record":{"source":{"id":"2408.02533","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-08-05T15:03:19Z","cross_cats_sorted":[],"title_canon_sha256":"79b0a3b7b4c6e956c1bf698999728285edbdbc45b0d78a2d049fd48f1a5b12ae","abstract_canon_sha256":"3fa804b445d3bb491cec62754838c02671ce251d6ac49b44502d8cfbf5824484"},"schema_version":"1.0"},"canonical_sha256":"2937818cd896ec984f24a731d1ab97de0b36c36e8482ffa7292a261c7a367db7","source":{"kind":"arxiv","id":"2408.02533","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.02533","created_at":"2026-07-05T08:52:14Z"},{"alias_kind":"arxiv_version","alias_value":"2408.02533v1","created_at":"2026-07-05T08:52:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.02533","created_at":"2026-07-05T08:52:14Z"},{"alias_kind":"pith_short_12","alias_value":"FE3YDDGYS3WJ","created_at":"2026-07-05T08:52:14Z"},{"alias_kind":"pith_short_16","alias_value":"FE3YDDGYS3WJQTZE","created_at":"2026-07-05T08:52:14Z"},{"alias_kind":"pith_short_8","alias_value":"FE3YDDGY","created_at":"2026-07-05T08:52:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:FE3YDDGYS3WJQTZEU4Y5DK4X3Y","target":"record","payload":{"canonical_record":{"source":{"id":"2408.02533","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-08-05T15:03:19Z","cross_cats_sorted":[],"title_canon_sha256":"79b0a3b7b4c6e956c1bf698999728285edbdbc45b0d78a2d049fd48f1a5b12ae","abstract_canon_sha256":"3fa804b445d3bb491cec62754838c02671ce251d6ac49b44502d8cfbf5824484"},"schema_version":"1.0"},"canonical_sha256":"2937818cd896ec984f24a731d1ab97de0b36c36e8482ffa7292a261c7a367db7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:52:14.085646Z","signature_b64":"S7KKMBq5W4Rh0/zjXqh0wjdz3hKjkNYTsh2iBXNtDfQZqH1hYs0jFfj6YzWSDsDE8bCrqwpHUbfBdC7YXD49Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2937818cd896ec984f24a731d1ab97de0b36c36e8482ffa7292a261c7a367db7","last_reissued_at":"2026-07-05T08:52:14.085243Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:52:14.085243Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2408.02533","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":"2026-07-05T08:52:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cvGiRCmvhpuFYVEWV9Ges7qJBG3tma31EwVSSjGC/sv7AfHdMiHGUIrY61zUrAR9pgsG5ahs2i5g0J12j/XDDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T00:16:37.670561Z"},"content_sha256":"bd020fed8b885f14c2da9a0fe9f8ad8a897ef084327fe4b13cde94631c22b241","schema_version":"1.0","event_id":"sha256:bd020fed8b885f14c2da9a0fe9f8ad8a897ef084327fe4b13cde94631c22b241"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:FE3YDDGYS3WJQTZEU4Y5DK4X3Y","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LMEMs for post-hoc analysis of HPO Benchmarking","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Anton Geburek, Danny Stoll, Frank Hutter, Neeratyoy Mallik, Xavier Bouthillier","submitted_at":"2024-08-05T15:03:19Z","abstract_excerpt":"The importance of tuning hyperparameters in Machine Learning (ML) and Deep Learning (DL) is established through empirical research and applications, evident from the increase in new hyperparameter optimization (HPO) algorithms and benchmarks steadily added by the community. However, current benchmarking practices using averaged performance across many datasets may obscure key differences between HPO methods, especially for pairwise comparisons. In this work, we apply Linear Mixed-Effect Models-based (LMEMs) significance testing for post-hoc analysis of HPO benchmarking runs. LMEMs allow flexib"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.02533","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/2408.02533/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":"2026-07-05T08:52:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"c296hru3uEdKNwq2igQqnjz7hYP7fwRdcIAkKP6ln49p9DlSu05qISEORvK7ZiNwiB0rEdUWc39HquLqZwC9BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T00:16:37.671069Z"},"content_sha256":"421e7f67daa71cce084a28d82cacbfcdca558ed9ac6dbea26d14223412ddb8c3","schema_version":"1.0","event_id":"sha256:421e7f67daa71cce084a28d82cacbfcdca558ed9ac6dbea26d14223412ddb8c3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FE3YDDGYS3WJQTZEU4Y5DK4X3Y/bundle.json","state_url":"https://pith.science/pith/FE3YDDGYS3WJQTZEU4Y5DK4X3Y/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FE3YDDGYS3WJQTZEU4Y5DK4X3Y/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":"2026-08-20T00:16:37Z","links":{"resolver":"https://pith.science/pith/FE3YDDGYS3WJQTZEU4Y5DK4X3Y","bundle":"https://pith.science/pith/FE3YDDGYS3WJQTZEU4Y5DK4X3Y/bundle.json","state":"https://pith.science/pith/FE3YDDGYS3WJQTZEU4Y5DK4X3Y/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FE3YDDGYS3WJQTZEU4Y5DK4X3Y/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:FE3YDDGYS3WJQTZEU4Y5DK4X3Y","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":"3fa804b445d3bb491cec62754838c02671ce251d6ac49b44502d8cfbf5824484","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-08-05T15:03:19Z","title_canon_sha256":"79b0a3b7b4c6e956c1bf698999728285edbdbc45b0d78a2d049fd48f1a5b12ae"},"schema_version":"1.0","source":{"id":"2408.02533","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.02533","created_at":"2026-07-05T08:52:14Z"},{"alias_kind":"arxiv_version","alias_value":"2408.02533v1","created_at":"2026-07-05T08:52:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.02533","created_at":"2026-07-05T08:52:14Z"},{"alias_kind":"pith_short_12","alias_value":"FE3YDDGYS3WJ","created_at":"2026-07-05T08:52:14Z"},{"alias_kind":"pith_short_16","alias_value":"FE3YDDGYS3WJQTZE","created_at":"2026-07-05T08:52:14Z"},{"alias_kind":"pith_short_8","alias_value":"FE3YDDGY","created_at":"2026-07-05T08:52:14Z"}],"graph_snapshots":[{"event_id":"sha256:421e7f67daa71cce084a28d82cacbfcdca558ed9ac6dbea26d14223412ddb8c3","target":"graph","created_at":"2026-07-05T08:52:14Z","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/2408.02533/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The importance of tuning hyperparameters in Machine Learning (ML) and Deep Learning (DL) is established through empirical research and applications, evident from the increase in new hyperparameter optimization (HPO) algorithms and benchmarks steadily added by the community. However, current benchmarking practices using averaged performance across many datasets may obscure key differences between HPO methods, especially for pairwise comparisons. In this work, we apply Linear Mixed-Effect Models-based (LMEMs) significance testing for post-hoc analysis of HPO benchmarking runs. LMEMs allow flexib","authors_text":"Anton Geburek, Danny Stoll, Frank Hutter, Neeratyoy Mallik, Xavier Bouthillier","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-08-05T15:03:19Z","title":"LMEMs for post-hoc analysis of HPO Benchmarking"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.02533","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:bd020fed8b885f14c2da9a0fe9f8ad8a897ef084327fe4b13cde94631c22b241","target":"record","created_at":"2026-07-05T08:52:14Z","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":"3fa804b445d3bb491cec62754838c02671ce251d6ac49b44502d8cfbf5824484","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-08-05T15:03:19Z","title_canon_sha256":"79b0a3b7b4c6e956c1bf698999728285edbdbc45b0d78a2d049fd48f1a5b12ae"},"schema_version":"1.0","source":{"id":"2408.02533","kind":"arxiv","version":1}},"canonical_sha256":"2937818cd896ec984f24a731d1ab97de0b36c36e8482ffa7292a261c7a367db7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2937818cd896ec984f24a731d1ab97de0b36c36e8482ffa7292a261c7a367db7","first_computed_at":"2026-07-05T08:52:14.085243Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:52:14.085243Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"S7KKMBq5W4Rh0/zjXqh0wjdz3hKjkNYTsh2iBXNtDfQZqH1hYs0jFfj6YzWSDsDE8bCrqwpHUbfBdC7YXD49Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:52:14.085646Z","signed_message":"canonical_sha256_bytes"},"source_id":"2408.02533","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bd020fed8b885f14c2da9a0fe9f8ad8a897ef084327fe4b13cde94631c22b241","sha256:421e7f67daa71cce084a28d82cacbfcdca558ed9ac6dbea26d14223412ddb8c3"],"state_sha256":"17bb07b7640e861e791b3f05b54b88ff6a419dae4b708141b346cd319b20129c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nBeDdaQFpDaN+/QdaVBJZ8HYYgiu58QksvfHUd1rHXG8cRoVeMQsMvIMMU1fU2amHr2gHzrpOR0O4S8AtHAmBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T00:16:37.675253Z","bundle_sha256":"019139217bcfa4e097145f02d14f40af3307fdfd612802d856dbe8363dea9d7e"}}