{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:H6GKSPVFWCMJNSLDN2EBJ5RIM2","short_pith_number":"pith:H6GKSPVF","canonical_record":{"source":{"id":"1802.07028","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-20T09:42:03Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"834bd84dfbe6c928ed0e475775bf970ebee2322eaae8b29d034fd5b749f6a837","abstract_canon_sha256":"ea9c43ff4fb7ec92fa788143055f200ffbad582536d479dd12dcf4e2d8ce8d3f"},"schema_version":"1.0"},"canonical_sha256":"3f8ca93ea5b09896c9636e8814f6286686e95bf872d1a89f4920640c10d7a1ea","source":{"kind":"arxiv","id":"1802.07028","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.07028","created_at":"2026-05-18T00:19:56Z"},{"alias_kind":"arxiv_version","alias_value":"1802.07028v2","created_at":"2026-05-18T00:19:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.07028","created_at":"2026-05-18T00:19:56Z"},{"alias_kind":"pith_short_12","alias_value":"H6GKSPVFWCMJ","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"H6GKSPVFWCMJNSLD","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"H6GKSPVF","created_at":"2026-05-18T12:32:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:H6GKSPVFWCMJNSLDN2EBJ5RIM2","target":"record","payload":{"canonical_record":{"source":{"id":"1802.07028","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-20T09:42:03Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"834bd84dfbe6c928ed0e475775bf970ebee2322eaae8b29d034fd5b749f6a837","abstract_canon_sha256":"ea9c43ff4fb7ec92fa788143055f200ffbad582536d479dd12dcf4e2d8ce8d3f"},"schema_version":"1.0"},"canonical_sha256":"3f8ca93ea5b09896c9636e8814f6286686e95bf872d1a89f4920640c10d7a1ea","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:19:56.366655Z","signature_b64":"ZBP08X2dFgEZAHNuX9FDDjsG+0lWWJwzoeGn0gYvFLyXGxNACgM6zn3VwqArDGCsV42jFrUdu0wzqjhdqpApCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3f8ca93ea5b09896c9636e8814f6286686e95bf872d1a89f4920640c10d7a1ea","last_reissued_at":"2026-05-18T00:19:56.365972Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:19:56.365972Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1802.07028","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":"2026-05-18T00:19:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8IAZX2q6H2ip43jdsrucLD1TEb+F10r1qByMyHe56U5xHihTwwmZb5b2zIyPxiPJheekaWGSqsaGWvgTE/wODg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T19:14:10.349550Z"},"content_sha256":"1f0a4889092ecae4e16763b1dd472e4bd0f107be05a186b186693c4dad972f9e","schema_version":"1.0","event_id":"sha256:1f0a4889092ecae4e16763b1dd472e4bd0f107be05a186b186693c4dad972f9e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:H6GKSPVFWCMJNSLDN2EBJ5RIM2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ilija Bogunovic, Jonathan Scarlett, Paul Rolland, Volkan Cevher","submitted_at":"2018-02-20T09:42:03Z","abstract_excerpt":"Bayesian optimization (BO) is a popular technique for sequential black-box function optimization, with applications including parameter tuning, robotics, environmental monitoring, and more. One of the most important challenges in BO is the development of algorithms that scale to high dimensions, which remains a key open problem despite recent progress. In this paper, we consider the approach of Kandasamy et al. (2015), in which the high-dimensional function decomposes as a sum of lower-dimensional functions on subsets of the underlying variables. In particular, we significantly generalize this"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.07028","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":""},"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":"2026-05-18T00:19:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T8waNG6hzUGawNsr0Rcc3mzfDPEgS7JTP3Enk6OLsUFXnzq4GcHBlU1M8z5eNiqSL0ofumH9glhXW2pxl1USDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T19:14:10.350239Z"},"content_sha256":"65207210d8463c4a01b19ff2f6ba5a94c6f1e31dc637e1f9c3534d875b3e08d0","schema_version":"1.0","event_id":"sha256:65207210d8463c4a01b19ff2f6ba5a94c6f1e31dc637e1f9c3534d875b3e08d0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/H6GKSPVFWCMJNSLDN2EBJ5RIM2/bundle.json","state_url":"https://pith.science/pith/H6GKSPVFWCMJNSLDN2EBJ5RIM2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/H6GKSPVFWCMJNSLDN2EBJ5RIM2/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":"2026-05-25T19:14:10Z","links":{"resolver":"https://pith.science/pith/H6GKSPVFWCMJNSLDN2EBJ5RIM2","bundle":"https://pith.science/pith/H6GKSPVFWCMJNSLDN2EBJ5RIM2/bundle.json","state":"https://pith.science/pith/H6GKSPVFWCMJNSLDN2EBJ5RIM2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/H6GKSPVFWCMJNSLDN2EBJ5RIM2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:H6GKSPVFWCMJNSLDN2EBJ5RIM2","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":"ea9c43ff4fb7ec92fa788143055f200ffbad582536d479dd12dcf4e2d8ce8d3f","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-20T09:42:03Z","title_canon_sha256":"834bd84dfbe6c928ed0e475775bf970ebee2322eaae8b29d034fd5b749f6a837"},"schema_version":"1.0","source":{"id":"1802.07028","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.07028","created_at":"2026-05-18T00:19:56Z"},{"alias_kind":"arxiv_version","alias_value":"1802.07028v2","created_at":"2026-05-18T00:19:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.07028","created_at":"2026-05-18T00:19:56Z"},{"alias_kind":"pith_short_12","alias_value":"H6GKSPVFWCMJ","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"H6GKSPVFWCMJNSLD","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"H6GKSPVF","created_at":"2026-05-18T12:32:28Z"}],"graph_snapshots":[{"event_id":"sha256:65207210d8463c4a01b19ff2f6ba5a94c6f1e31dc637e1f9c3534d875b3e08d0","target":"graph","created_at":"2026-05-18T00:19:56Z","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"},"paper":{"abstract_excerpt":"Bayesian optimization (BO) is a popular technique for sequential black-box function optimization, with applications including parameter tuning, robotics, environmental monitoring, and more. One of the most important challenges in BO is the development of algorithms that scale to high dimensions, which remains a key open problem despite recent progress. In this paper, we consider the approach of Kandasamy et al. (2015), in which the high-dimensional function decomposes as a sum of lower-dimensional functions on subsets of the underlying variables. In particular, we significantly generalize this","authors_text":"Ilija Bogunovic, Jonathan Scarlett, Paul Rolland, Volkan Cevher","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-20T09:42:03Z","title":"High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.07028","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":"sha256:1f0a4889092ecae4e16763b1dd472e4bd0f107be05a186b186693c4dad972f9e","target":"record","created_at":"2026-05-18T00:19:56Z","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":"ea9c43ff4fb7ec92fa788143055f200ffbad582536d479dd12dcf4e2d8ce8d3f","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-20T09:42:03Z","title_canon_sha256":"834bd84dfbe6c928ed0e475775bf970ebee2322eaae8b29d034fd5b749f6a837"},"schema_version":"1.0","source":{"id":"1802.07028","kind":"arxiv","version":2}},"canonical_sha256":"3f8ca93ea5b09896c9636e8814f6286686e95bf872d1a89f4920640c10d7a1ea","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3f8ca93ea5b09896c9636e8814f6286686e95bf872d1a89f4920640c10d7a1ea","first_computed_at":"2026-05-18T00:19:56.365972Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:19:56.365972Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZBP08X2dFgEZAHNuX9FDDjsG+0lWWJwzoeGn0gYvFLyXGxNACgM6zn3VwqArDGCsV42jFrUdu0wzqjhdqpApCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:19:56.366655Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.07028","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1f0a4889092ecae4e16763b1dd472e4bd0f107be05a186b186693c4dad972f9e","sha256:65207210d8463c4a01b19ff2f6ba5a94c6f1e31dc637e1f9c3534d875b3e08d0"],"state_sha256":"ade1925fd69c7abe151ec2f6b039ff30a5b1b13d667820aaae054d278db5e580"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"r2e8f40sPNVL8c0TIfuM1Itn2KuZaUX4TldrwzCQ5MobO+AtGJZYiZIlhoaCw/Mak3ePPk76JLGI9P5gjGVhCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T19:14:10.356935Z","bundle_sha256":"57ff0b4f8f746e0f7ca393aedacefd695e02a10b166475ad9c0084f53af02b69"}}