{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:KGJIBAPTP5KP5Q26SOKDXXTBOR","short_pith_number":"pith:KGJIBAPT","canonical_record":{"source":{"id":"1803.05036","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-03-13T20:34:23Z","cross_cats_sorted":[],"title_canon_sha256":"f7a2b26136b3a123ee7c59dc5093adeb1c91658afa279bc4a0abb9adbd6d24d6","abstract_canon_sha256":"fdb7a9c22823cdcdce357f96cae4bb696c48628d318ea8e15530ad1e3b877c17"},"schema_version":"1.0"},"canonical_sha256":"51928081f37f54fec35e93943bde61745b2929830892e98d772f99c8baa69987","source":{"kind":"arxiv","id":"1803.05036","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.05036","created_at":"2026-05-18T00:21:02Z"},{"alias_kind":"arxiv_version","alias_value":"1803.05036v1","created_at":"2026-05-18T00:21:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.05036","created_at":"2026-05-18T00:21:02Z"},{"alias_kind":"pith_short_12","alias_value":"KGJIBAPTP5KP","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_16","alias_value":"KGJIBAPTP5KP5Q26","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_8","alias_value":"KGJIBAPT","created_at":"2026-05-18T12:32:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:KGJIBAPTP5KP5Q26SOKDXXTBOR","target":"record","payload":{"canonical_record":{"source":{"id":"1803.05036","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-03-13T20:34:23Z","cross_cats_sorted":[],"title_canon_sha256":"f7a2b26136b3a123ee7c59dc5093adeb1c91658afa279bc4a0abb9adbd6d24d6","abstract_canon_sha256":"fdb7a9c22823cdcdce357f96cae4bb696c48628d318ea8e15530ad1e3b877c17"},"schema_version":"1.0"},"canonical_sha256":"51928081f37f54fec35e93943bde61745b2929830892e98d772f99c8baa69987","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:21:02.233963Z","signature_b64":"Bk3Dt5GD/HAVlJgFV/S2eok+A3LYj6FiElRal4t8d0/+6EF+Rcy8y0Oi4u/vnssS5uXrip7MFEmdzlt9h8vICg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"51928081f37f54fec35e93943bde61745b2929830892e98d772f99c8baa69987","last_reissued_at":"2026-05-18T00:21:02.233547Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:21:02.233547Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1803.05036","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":"2026-05-18T00:21:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YPEi+ax0p6SVQh9zVEj9qUsV4ABX2HJA3Ji/r86KyyjVpbxTsgoX5QgFdudqcY3nOlmC0W1OW57F6xwJuVUkCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T18:42:36.597995Z"},"content_sha256":"8bf81eea2949a2c0fb320b6df666a0aff6a0e68d696b3a1d78f0bdeccb1a891b","schema_version":"1.0","event_id":"sha256:8bf81eea2949a2c0fb320b6df666a0aff6a0e68d696b3a1d78f0bdeccb1a891b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:KGJIBAPTP5KP5Q26SOKDXXTBOR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Variational zero-inflated Gaussian processes with sparse kernels","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Markus Heinonen, Pashupati Hegde, Samuel Kaski","submitted_at":"2018-03-13T20:34:23Z","abstract_excerpt":"Zero-inflated datasets, which have an excess of zero outputs, are commonly encountered in problems such as climate or rare event modelling. Conventional machine learning approaches tend to overestimate the non-zeros leading to poor performance. We propose a novel model family of zero-inflated Gaussian processes (ZiGP) for such zero-inflated datasets, produced by sparse kernels through learning a latent probit Gaussian process that can zero out kernel rows and columns whenever the signal is absent. The ZiGPs are particularly useful for making the powerful Gaussian process networks more interpre"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.05036","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":""},"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":"2026-05-18T00:21:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gb6iI4cJjyeO6G1LmeJxA6BMEvAsQsHBoRbaWUvIB6nR/yDRajvhiU49WIWFJOLbQEyTXvKbxanDucI1UhJpCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T18:42:36.598332Z"},"content_sha256":"5f91a27c178905d64176b900fc4503fc752a9e69fef8938baa659c784213b9e5","schema_version":"1.0","event_id":"sha256:5f91a27c178905d64176b900fc4503fc752a9e69fef8938baa659c784213b9e5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KGJIBAPTP5KP5Q26SOKDXXTBOR/bundle.json","state_url":"https://pith.science/pith/KGJIBAPTP5KP5Q26SOKDXXTBOR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KGJIBAPTP5KP5Q26SOKDXXTBOR/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":"2026-06-02T18:42:36Z","links":{"resolver":"https://pith.science/pith/KGJIBAPTP5KP5Q26SOKDXXTBOR","bundle":"https://pith.science/pith/KGJIBAPTP5KP5Q26SOKDXXTBOR/bundle.json","state":"https://pith.science/pith/KGJIBAPTP5KP5Q26SOKDXXTBOR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KGJIBAPTP5KP5Q26SOKDXXTBOR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:KGJIBAPTP5KP5Q26SOKDXXTBOR","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":"fdb7a9c22823cdcdce357f96cae4bb696c48628d318ea8e15530ad1e3b877c17","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-03-13T20:34:23Z","title_canon_sha256":"f7a2b26136b3a123ee7c59dc5093adeb1c91658afa279bc4a0abb9adbd6d24d6"},"schema_version":"1.0","source":{"id":"1803.05036","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.05036","created_at":"2026-05-18T00:21:02Z"},{"alias_kind":"arxiv_version","alias_value":"1803.05036v1","created_at":"2026-05-18T00:21:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.05036","created_at":"2026-05-18T00:21:02Z"},{"alias_kind":"pith_short_12","alias_value":"KGJIBAPTP5KP","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_16","alias_value":"KGJIBAPTP5KP5Q26","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_8","alias_value":"KGJIBAPT","created_at":"2026-05-18T12:32:33Z"}],"graph_snapshots":[{"event_id":"sha256:5f91a27c178905d64176b900fc4503fc752a9e69fef8938baa659c784213b9e5","target":"graph","created_at":"2026-05-18T00:21:02Z","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":"Zero-inflated datasets, which have an excess of zero outputs, are commonly encountered in problems such as climate or rare event modelling. Conventional machine learning approaches tend to overestimate the non-zeros leading to poor performance. We propose a novel model family of zero-inflated Gaussian processes (ZiGP) for such zero-inflated datasets, produced by sparse kernels through learning a latent probit Gaussian process that can zero out kernel rows and columns whenever the signal is absent. The ZiGPs are particularly useful for making the powerful Gaussian process networks more interpre","authors_text":"Markus Heinonen, Pashupati Hegde, Samuel Kaski","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-03-13T20:34:23Z","title":"Variational zero-inflated Gaussian processes with sparse kernels"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.05036","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":"sha256:8bf81eea2949a2c0fb320b6df666a0aff6a0e68d696b3a1d78f0bdeccb1a891b","target":"record","created_at":"2026-05-18T00:21:02Z","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":"fdb7a9c22823cdcdce357f96cae4bb696c48628d318ea8e15530ad1e3b877c17","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-03-13T20:34:23Z","title_canon_sha256":"f7a2b26136b3a123ee7c59dc5093adeb1c91658afa279bc4a0abb9adbd6d24d6"},"schema_version":"1.0","source":{"id":"1803.05036","kind":"arxiv","version":1}},"canonical_sha256":"51928081f37f54fec35e93943bde61745b2929830892e98d772f99c8baa69987","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"51928081f37f54fec35e93943bde61745b2929830892e98d772f99c8baa69987","first_computed_at":"2026-05-18T00:21:02.233547Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:21:02.233547Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Bk3Dt5GD/HAVlJgFV/S2eok+A3LYj6FiElRal4t8d0/+6EF+Rcy8y0Oi4u/vnssS5uXrip7MFEmdzlt9h8vICg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:21:02.233963Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.05036","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8bf81eea2949a2c0fb320b6df666a0aff6a0e68d696b3a1d78f0bdeccb1a891b","sha256:5f91a27c178905d64176b900fc4503fc752a9e69fef8938baa659c784213b9e5"],"state_sha256":"9ca7e206c63beef8b85985dcfaeab30ea9582a08d32ebbf6ffd57525675043f6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EgTD1+Ni80db8HzGVpbPV0dAlb0KRdbreSBqX+IGku/O4hkmNjoxoYurfUvtgvFGzgdtXy8WU6jBot/jQpzWDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T18:42:36.600092Z","bundle_sha256":"7e3fd4a6c80f2371f9895808b3c0e62d575f3ef553002eea85277964a31a2d8e"}}