{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:5ESWUCOSFFNSZ2WKVYIXMG65BX","short_pith_number":"pith:5ESWUCOS","canonical_record":{"source":{"id":"2206.03130","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-07T09:14:24Z","cross_cats_sorted":[],"title_canon_sha256":"780d46ffa3e0ec4e80989d5428603b482fe7287f7d77054a2d6af3b7e92a4abc","abstract_canon_sha256":"662cac5cf4ed8ee6391a7a2ae65c1f63e17f04d292c62767eed5d71be3a0f5fb"},"schema_version":"1.0"},"canonical_sha256":"e9256a09d2295b2ceacaae11761bdd0dde0b1e1e3a13ee6eadc4977b2802395a","source":{"kind":"arxiv","id":"2206.03130","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.03130","created_at":"2026-07-05T04:39:50Z"},{"alias_kind":"arxiv_version","alias_value":"2206.03130v2","created_at":"2026-07-05T04:39:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.03130","created_at":"2026-07-05T04:39:50Z"},{"alias_kind":"pith_short_12","alias_value":"5ESWUCOSFFNS","created_at":"2026-07-05T04:39:50Z"},{"alias_kind":"pith_short_16","alias_value":"5ESWUCOSFFNSZ2WK","created_at":"2026-07-05T04:39:50Z"},{"alias_kind":"pith_short_8","alias_value":"5ESWUCOS","created_at":"2026-07-05T04:39:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:5ESWUCOSFFNSZ2WKVYIXMG65BX","target":"record","payload":{"canonical_record":{"source":{"id":"2206.03130","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-07T09:14:24Z","cross_cats_sorted":[],"title_canon_sha256":"780d46ffa3e0ec4e80989d5428603b482fe7287f7d77054a2d6af3b7e92a4abc","abstract_canon_sha256":"662cac5cf4ed8ee6391a7a2ae65c1f63e17f04d292c62767eed5d71be3a0f5fb"},"schema_version":"1.0"},"canonical_sha256":"e9256a09d2295b2ceacaae11761bdd0dde0b1e1e3a13ee6eadc4977b2802395a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:39:50.391975Z","signature_b64":"pDwvHc5RbNhA1bgvDpw49PTHhyv3FJIIVtWUKcahc3s1b6PCArQPJw4HT33kZ+bzm1aEgA1jqgLN4Teaiz7VDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e9256a09d2295b2ceacaae11761bdd0dde0b1e1e3a13ee6eadc4977b2802395a","last_reissued_at":"2026-07-05T04:39:50.391623Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:39:50.391623Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2206.03130","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:39:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zifrn5f7DZxSzqAmmzyIHjStMMC7p4AtmJ939aU6M5wHasPdPM6r4XZ5Ti0mx1J4Fbm3y9zRAL9SwjUmWBSNDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T15:18:40.640573Z"},"content_sha256":"4939e291fb87a34bf4b9c2bfc09fc6536d6b039959f70ec659f995360bfcf78a","schema_version":"1.0","event_id":"sha256:4939e291fb87a34bf4b9c2bfc09fc6536d6b039959f70ec659f995360bfcf78a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:5ESWUCOSFFNSZ2WKVYIXMG65BX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Towards Meta-learned Algorithm Selection using Implicit Fidelity Information","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Aditya Mohan, Marius Lindauer, Tim Ruhkopf","submitted_at":"2022-06-07T09:14:24Z","abstract_excerpt":"Automatically selecting the best performing algorithm for a given dataset or ranking multiple algorithms by their expected performance supports users in developing new machine learning applications. Most approaches for this problem rely on pre-computed dataset meta-features and landmarking performances to capture the salient topology of the datasets and those topologies that the algorithms attend to. Landmarking usually exploits cheap algorithms not necessarily in the pool of candidate algorithms to get inexpensive approximations of the topology. While somewhat indicative, hand-crafted dataset"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.03130","kind":"arxiv","version":2},"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/2206.03130/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":"2026-07-05T04:39:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6iitZTMpL1luAUE92zkpQ9rMl+GmZOd1skqANAjLM2cScjMg7t2kY0kVFZV33vqbqgYH+Pw/Gpd9LEj0g88XCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T15:18:40.640951Z"},"content_sha256":"fc2e0d5a6d35fb25e8293d66560f0ea2401b801c6c4afe160d6e9a6f707935ef","schema_version":"1.0","event_id":"sha256:fc2e0d5a6d35fb25e8293d66560f0ea2401b801c6c4afe160d6e9a6f707935ef"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5ESWUCOSFFNSZ2WKVYIXMG65BX/bundle.json","state_url":"https://pith.science/pith/5ESWUCOSFFNSZ2WKVYIXMG65BX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5ESWUCOSFFNSZ2WKVYIXMG65BX/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":"2026-07-10T15:18:40Z","links":{"resolver":"https://pith.science/pith/5ESWUCOSFFNSZ2WKVYIXMG65BX","bundle":"https://pith.science/pith/5ESWUCOSFFNSZ2WKVYIXMG65BX/bundle.json","state":"https://pith.science/pith/5ESWUCOSFFNSZ2WKVYIXMG65BX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5ESWUCOSFFNSZ2WKVYIXMG65BX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:5ESWUCOSFFNSZ2WKVYIXMG65BX","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":"662cac5cf4ed8ee6391a7a2ae65c1f63e17f04d292c62767eed5d71be3a0f5fb","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-07T09:14:24Z","title_canon_sha256":"780d46ffa3e0ec4e80989d5428603b482fe7287f7d77054a2d6af3b7e92a4abc"},"schema_version":"1.0","source":{"id":"2206.03130","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.03130","created_at":"2026-07-05T04:39:50Z"},{"alias_kind":"arxiv_version","alias_value":"2206.03130v2","created_at":"2026-07-05T04:39:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.03130","created_at":"2026-07-05T04:39:50Z"},{"alias_kind":"pith_short_12","alias_value":"5ESWUCOSFFNS","created_at":"2026-07-05T04:39:50Z"},{"alias_kind":"pith_short_16","alias_value":"5ESWUCOSFFNSZ2WK","created_at":"2026-07-05T04:39:50Z"},{"alias_kind":"pith_short_8","alias_value":"5ESWUCOS","created_at":"2026-07-05T04:39:50Z"}],"graph_snapshots":[{"event_id":"sha256:fc2e0d5a6d35fb25e8293d66560f0ea2401b801c6c4afe160d6e9a6f707935ef","target":"graph","created_at":"2026-07-05T04:39:50Z","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/2206.03130/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Automatically selecting the best performing algorithm for a given dataset or ranking multiple algorithms by their expected performance supports users in developing new machine learning applications. Most approaches for this problem rely on pre-computed dataset meta-features and landmarking performances to capture the salient topology of the datasets and those topologies that the algorithms attend to. Landmarking usually exploits cheap algorithms not necessarily in the pool of candidate algorithms to get inexpensive approximations of the topology. While somewhat indicative, hand-crafted dataset","authors_text":"Aditya Mohan, Marius Lindauer, Tim Ruhkopf","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-07T09:14:24Z","title":"Towards Meta-learned Algorithm Selection using Implicit Fidelity Information"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.03130","kind":"arxiv","version":2},"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":"sha256:4939e291fb87a34bf4b9c2bfc09fc6536d6b039959f70ec659f995360bfcf78a","target":"record","created_at":"2026-07-05T04:39:50Z","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":"662cac5cf4ed8ee6391a7a2ae65c1f63e17f04d292c62767eed5d71be3a0f5fb","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-07T09:14:24Z","title_canon_sha256":"780d46ffa3e0ec4e80989d5428603b482fe7287f7d77054a2d6af3b7e92a4abc"},"schema_version":"1.0","source":{"id":"2206.03130","kind":"arxiv","version":2}},"canonical_sha256":"e9256a09d2295b2ceacaae11761bdd0dde0b1e1e3a13ee6eadc4977b2802395a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e9256a09d2295b2ceacaae11761bdd0dde0b1e1e3a13ee6eadc4977b2802395a","first_computed_at":"2026-07-05T04:39:50.391623Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:39:50.391623Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pDwvHc5RbNhA1bgvDpw49PTHhyv3FJIIVtWUKcahc3s1b6PCArQPJw4HT33kZ+bzm1aEgA1jqgLN4Teaiz7VDg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:39:50.391975Z","signed_message":"canonical_sha256_bytes"},"source_id":"2206.03130","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4939e291fb87a34bf4b9c2bfc09fc6536d6b039959f70ec659f995360bfcf78a","sha256:fc2e0d5a6d35fb25e8293d66560f0ea2401b801c6c4afe160d6e9a6f707935ef"],"state_sha256":"20417056f890e6937af1e327e1fcd3886e79ec9762b1f20e27a520d6e0b8c97d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+x4dzrmUY7hDAfPCtgzB0dcPBTKridL5CNh0dnz9A5Omyt2w9aE8o1nI6LzzWvEBB5NM/8A+arxfyhcjzu/iBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-10T15:18:40.642847Z","bundle_sha256":"eadc32c36edd1f3d17c5583e06315509eed094aa946ac783560bf3905327e832"}}