{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:OHMCVLGQBSJ7NW5BVSIJRDXFYC","short_pith_number":"pith:OHMCVLGQ","canonical_record":{"source":{"id":"2402.09849","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-15T10:11:28Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"27f14c75bd0b5dea32c96e844f1d46fb7928b2fdf875906853c52847f0647a1b","abstract_canon_sha256":"0a73809c10531a9f849d0643b3ad0166cec8ede1ca83465b6df06b1734985f01"},"schema_version":"1.0"},"canonical_sha256":"71d82aacd00c93f6dba1ac90988ee5c08565f16e7cf9f59c6ceb0d6ed9690cb8","source":{"kind":"arxiv","id":"2402.09849","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.09849","created_at":"2026-07-05T07:45:37Z"},{"alias_kind":"arxiv_version","alias_value":"2402.09849v1","created_at":"2026-07-05T07:45:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.09849","created_at":"2026-07-05T07:45:37Z"},{"alias_kind":"pith_short_12","alias_value":"OHMCVLGQBSJ7","created_at":"2026-07-05T07:45:37Z"},{"alias_kind":"pith_short_16","alias_value":"OHMCVLGQBSJ7NW5B","created_at":"2026-07-05T07:45:37Z"},{"alias_kind":"pith_short_8","alias_value":"OHMCVLGQ","created_at":"2026-07-05T07:45:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:OHMCVLGQBSJ7NW5BVSIJRDXFYC","target":"record","payload":{"canonical_record":{"source":{"id":"2402.09849","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-15T10:11:28Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"27f14c75bd0b5dea32c96e844f1d46fb7928b2fdf875906853c52847f0647a1b","abstract_canon_sha256":"0a73809c10531a9f849d0643b3ad0166cec8ede1ca83465b6df06b1734985f01"},"schema_version":"1.0"},"canonical_sha256":"71d82aacd00c93f6dba1ac90988ee5c08565f16e7cf9f59c6ceb0d6ed9690cb8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:45:37.810566Z","signature_b64":"9vAtJwxyXrI6/+6TflzOO4OIyhh2B2wxlmx/j4MxhQIv3CAeGGOWOyOjiuHduF0u/hUxygwH7+vHxjhBcXkFAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"71d82aacd00c93f6dba1ac90988ee5c08565f16e7cf9f59c6ceb0d6ed9690cb8","last_reissued_at":"2026-07-05T07:45:37.810060Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:45:37.810060Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.09849","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":"2026-07-05T07:45:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9Yfju51I9t8OYIjWT42/5wbLDKKTOIXKW/zm7/ycAdjM3a7x35J/7ykNolVKWLIl6D/LzEOUoDU7oq0it7v2AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T00:02:26.698559Z"},"content_sha256":"40294907c1b182e81217f6b68f08125814ef66cc8825c7e8329ad90f6c13fd17","schema_version":"1.0","event_id":"sha256:40294907c1b182e81217f6b68f08125814ef66cc8825c7e8329ad90f6c13fd17"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:OHMCVLGQBSJ7NW5BVSIJRDXFYC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Recommendations for Baselines and Benchmarking Approximate Gaussian Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Artem Artemev, Marcel Wagenl\\\"ander, Mark van der Wilk, Rudolfs Grobins, Sebastian W. Ober","submitted_at":"2024-02-15T10:11:28Z","abstract_excerpt":"Gaussian processes (GPs) are a mature and widely-used component of the ML toolbox. One of their desirable qualities is automatic hyperparameter selection, which allows for training without user intervention. However, in many realistic settings, approximations are typically needed, which typically do require tuning. We argue that this requirement for tuning complicates evaluation, which has led to a lack of a clear recommendations on which method should be used in which situation. To address this, we make recommendations for comparing GP approximations based on a specification of what a user sh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.09849","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/2402.09849/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":"2026-07-05T07:45:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wqmBg2U8nrGSFte+9u2Ybwuo94Gehzb0TMCEqASMALqm3Aj9nWub3upUlYZ0sdF0Hgnk3MNbBFVCpPD5gbWyCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T00:02:26.699067Z"},"content_sha256":"0425a0d2e5133f8aaf57a4daf622a0c3f53c7d804f598f8fd6f5ca8c1a50fb24","schema_version":"1.0","event_id":"sha256:0425a0d2e5133f8aaf57a4daf622a0c3f53c7d804f598f8fd6f5ca8c1a50fb24"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OHMCVLGQBSJ7NW5BVSIJRDXFYC/bundle.json","state_url":"https://pith.science/pith/OHMCVLGQBSJ7NW5BVSIJRDXFYC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OHMCVLGQBSJ7NW5BVSIJRDXFYC/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":"2026-08-20T00:02:26Z","links":{"resolver":"https://pith.science/pith/OHMCVLGQBSJ7NW5BVSIJRDXFYC","bundle":"https://pith.science/pith/OHMCVLGQBSJ7NW5BVSIJRDXFYC/bundle.json","state":"https://pith.science/pith/OHMCVLGQBSJ7NW5BVSIJRDXFYC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OHMCVLGQBSJ7NW5BVSIJRDXFYC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:OHMCVLGQBSJ7NW5BVSIJRDXFYC","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":"0a73809c10531a9f849d0643b3ad0166cec8ede1ca83465b6df06b1734985f01","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-15T10:11:28Z","title_canon_sha256":"27f14c75bd0b5dea32c96e844f1d46fb7928b2fdf875906853c52847f0647a1b"},"schema_version":"1.0","source":{"id":"2402.09849","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.09849","created_at":"2026-07-05T07:45:37Z"},{"alias_kind":"arxiv_version","alias_value":"2402.09849v1","created_at":"2026-07-05T07:45:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.09849","created_at":"2026-07-05T07:45:37Z"},{"alias_kind":"pith_short_12","alias_value":"OHMCVLGQBSJ7","created_at":"2026-07-05T07:45:37Z"},{"alias_kind":"pith_short_16","alias_value":"OHMCVLGQBSJ7NW5B","created_at":"2026-07-05T07:45:37Z"},{"alias_kind":"pith_short_8","alias_value":"OHMCVLGQ","created_at":"2026-07-05T07:45:37Z"}],"graph_snapshots":[{"event_id":"sha256:0425a0d2e5133f8aaf57a4daf622a0c3f53c7d804f598f8fd6f5ca8c1a50fb24","target":"graph","created_at":"2026-07-05T07:45:37Z","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/2402.09849/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Gaussian processes (GPs) are a mature and widely-used component of the ML toolbox. One of their desirable qualities is automatic hyperparameter selection, which allows for training without user intervention. However, in many realistic settings, approximations are typically needed, which typically do require tuning. We argue that this requirement for tuning complicates evaluation, which has led to a lack of a clear recommendations on which method should be used in which situation. To address this, we make recommendations for comparing GP approximations based on a specification of what a user sh","authors_text":"Artem Artemev, Marcel Wagenl\\\"ander, Mark van der Wilk, Rudolfs Grobins, Sebastian W. Ober","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-15T10:11:28Z","title":"Recommendations for Baselines and Benchmarking Approximate Gaussian Processes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.09849","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":"sha256:40294907c1b182e81217f6b68f08125814ef66cc8825c7e8329ad90f6c13fd17","target":"record","created_at":"2026-07-05T07:45:37Z","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":"0a73809c10531a9f849d0643b3ad0166cec8ede1ca83465b6df06b1734985f01","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-15T10:11:28Z","title_canon_sha256":"27f14c75bd0b5dea32c96e844f1d46fb7928b2fdf875906853c52847f0647a1b"},"schema_version":"1.0","source":{"id":"2402.09849","kind":"arxiv","version":1}},"canonical_sha256":"71d82aacd00c93f6dba1ac90988ee5c08565f16e7cf9f59c6ceb0d6ed9690cb8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"71d82aacd00c93f6dba1ac90988ee5c08565f16e7cf9f59c6ceb0d6ed9690cb8","first_computed_at":"2026-07-05T07:45:37.810060Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:45:37.810060Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9vAtJwxyXrI6/+6TflzOO4OIyhh2B2wxlmx/j4MxhQIv3CAeGGOWOyOjiuHduF0u/hUxygwH7+vHxjhBcXkFAA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:45:37.810566Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.09849","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:40294907c1b182e81217f6b68f08125814ef66cc8825c7e8329ad90f6c13fd17","sha256:0425a0d2e5133f8aaf57a4daf622a0c3f53c7d804f598f8fd6f5ca8c1a50fb24"],"state_sha256":"6b27c2b577be3df219819f2c5a132d3b855828b225b3cff4b92dee216d7f3ffe"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"O0w/zyHac4EP22YGqnIWToCZFuEnv1dhdb1OKAKaO9BuixUbTTDzDwlSuqmZ8+sJoX6BMIgMD/1rGh+yKxmHAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T00:02:26.704015Z","bundle_sha256":"9911f6875a65a2f5b89e6831aeb3898074e3efb456b4a110a06de50acba41ed2"}}