{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:YETHTHXWGUGCU6AEXVGX2QLNGJ","short_pith_number":"pith:YETHTHXW","canonical_record":{"source":{"id":"1812.05678","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-12-13T20:36:38Z","cross_cats_sorted":[],"title_canon_sha256":"9527e542a2e2b40b747f414e280843adef658a75a98548af745ce139ce1a2ae2","abstract_canon_sha256":"593fbafc92f0271ca23e7443ae791f480fbbd5920a47c630d3ee4dd83e843306"},"schema_version":"1.0"},"canonical_sha256":"c126799ef6350c2a7804bd4d7d416d3276620a360826f7644defa5ae4259c376","source":{"kind":"arxiv","id":"1812.05678","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.05678","created_at":"2026-06-04T01:08:23Z"},{"alias_kind":"arxiv_version","alias_value":"1812.05678v5","created_at":"2026-06-04T01:08:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.05678","created_at":"2026-06-04T01:08:23Z"},{"alias_kind":"pith_short_12","alias_value":"YETHTHXWGUGC","created_at":"2026-06-04T01:08:23Z"},{"alias_kind":"pith_short_16","alias_value":"YETHTHXWGUGCU6AE","created_at":"2026-06-04T01:08:23Z"},{"alias_kind":"pith_short_8","alias_value":"YETHTHXW","created_at":"2026-06-04T01:08:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:YETHTHXWGUGCU6AEXVGX2QLNGJ","target":"record","payload":{"canonical_record":{"source":{"id":"1812.05678","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-12-13T20:36:38Z","cross_cats_sorted":[],"title_canon_sha256":"9527e542a2e2b40b747f414e280843adef658a75a98548af745ce139ce1a2ae2","abstract_canon_sha256":"593fbafc92f0271ca23e7443ae791f480fbbd5920a47c630d3ee4dd83e843306"},"schema_version":"1.0"},"canonical_sha256":"c126799ef6350c2a7804bd4d7d416d3276620a360826f7644defa5ae4259c376","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-04T01:08:23.662330Z","signature_b64":"IqJmS/wCKhkjT7GwOXSymzyheNhsysCKMsoOZgACnF7UEIYLngRsoQU4JUed8ifBflWVkeoENsxFWk3KsJ5vDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c126799ef6350c2a7804bd4d7d416d3276620a360826f7644defa5ae4259c376","last_reissued_at":"2026-06-04T01:08:23.661755Z","signature_status":"signed_v1","first_computed_at":"2026-06-04T01:08:23.661755Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1812.05678","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-04T01:08:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0w+40pKefC++fSuHP+al8HG6TCPcIt1pWbn1UZFupwElglUKo1m07IcdefuLyHCe1V8/FuxXzutkS6H8xYXBCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-21T01:33:45.634630Z"},"content_sha256":"be72b9451d35622b06d096446494b117603b37f8c517f76da96f38617b97a94c","schema_version":"1.0","event_id":"sha256:be72b9451d35622b06d096446494b117603b37f8c517f76da96f38617b97a94c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:YETHTHXWGUGCU6AEXVGX2QLNGJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Objective-Driven Ensembles: Bridging the Gap Between Interpretable Sparsity and Algorithmic Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Anthony Christidis, Ruben Zamar, Stefan Van Aelst","submitted_at":"2018-12-13T20:36:38Z","abstract_excerpt":"Sparse methods (e.g., Best Subset Selection, Elastic Net) are the standard approach for obtaining interpretable models, but they can suffer from high variance and vulnerability to spurious correlations. Alternatively, algorithmic ensembles (e.g., Random Forests, Gradient Boosting) achieve high prediction accuracy but yield uninterpretable black boxes driven by randomization or sequential residual fitting. In recent years, a unifying paradigm has emerged: Objective-Driven Ensembles. By generalizing best subset selection into a joint mathematical optimization problem, this approach generates int"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.05678","kind":"arxiv","version":5},"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/1812.05678/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":"2026-06-04T01:08:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yL1sx4BvKQqF5MgSCBN39JvwqfAjd9+9C3BMb2c9tk6kbvmy9M3Bv7EbxM7RUTgFfmpm7uYeV2dIGDatCcOwDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-21T01:33:45.635002Z"},"content_sha256":"67bab5c7f04e4304a8c72cfb5c44cc7a9f1448f2bb8b7772a54e5a4119fb094c","schema_version":"1.0","event_id":"sha256:67bab5c7f04e4304a8c72cfb5c44cc7a9f1448f2bb8b7772a54e5a4119fb094c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YETHTHXWGUGCU6AEXVGX2QLNGJ/bundle.json","state_url":"https://pith.science/pith/YETHTHXWGUGCU6AEXVGX2QLNGJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YETHTHXWGUGCU6AEXVGX2QLNGJ/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":"2026-06-21T01:33:45Z","links":{"resolver":"https://pith.science/pith/YETHTHXWGUGCU6AEXVGX2QLNGJ","bundle":"https://pith.science/pith/YETHTHXWGUGCU6AEXVGX2QLNGJ/bundle.json","state":"https://pith.science/pith/YETHTHXWGUGCU6AEXVGX2QLNGJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YETHTHXWGUGCU6AEXVGX2QLNGJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:YETHTHXWGUGCU6AEXVGX2QLNGJ","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":"593fbafc92f0271ca23e7443ae791f480fbbd5920a47c630d3ee4dd83e843306","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-12-13T20:36:38Z","title_canon_sha256":"9527e542a2e2b40b747f414e280843adef658a75a98548af745ce139ce1a2ae2"},"schema_version":"1.0","source":{"id":"1812.05678","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.05678","created_at":"2026-06-04T01:08:23Z"},{"alias_kind":"arxiv_version","alias_value":"1812.05678v5","created_at":"2026-06-04T01:08:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.05678","created_at":"2026-06-04T01:08:23Z"},{"alias_kind":"pith_short_12","alias_value":"YETHTHXWGUGC","created_at":"2026-06-04T01:08:23Z"},{"alias_kind":"pith_short_16","alias_value":"YETHTHXWGUGCU6AE","created_at":"2026-06-04T01:08:23Z"},{"alias_kind":"pith_short_8","alias_value":"YETHTHXW","created_at":"2026-06-04T01:08:23Z"}],"graph_snapshots":[{"event_id":"sha256:67bab5c7f04e4304a8c72cfb5c44cc7a9f1448f2bb8b7772a54e5a4119fb094c","target":"graph","created_at":"2026-06-04T01:08:23Z","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/1812.05678/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Sparse methods (e.g., Best Subset Selection, Elastic Net) are the standard approach for obtaining interpretable models, but they can suffer from high variance and vulnerability to spurious correlations. Alternatively, algorithmic ensembles (e.g., Random Forests, Gradient Boosting) achieve high prediction accuracy but yield uninterpretable black boxes driven by randomization or sequential residual fitting. In recent years, a unifying paradigm has emerged: Objective-Driven Ensembles. By generalizing best subset selection into a joint mathematical optimization problem, this approach generates int","authors_text":"Anthony Christidis, Ruben Zamar, Stefan Van Aelst","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-12-13T20:36:38Z","title":"Objective-Driven Ensembles: Bridging the Gap Between Interpretable Sparsity and Algorithmic Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.05678","kind":"arxiv","version":5},"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:be72b9451d35622b06d096446494b117603b37f8c517f76da96f38617b97a94c","target":"record","created_at":"2026-06-04T01:08:23Z","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":"593fbafc92f0271ca23e7443ae791f480fbbd5920a47c630d3ee4dd83e843306","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-12-13T20:36:38Z","title_canon_sha256":"9527e542a2e2b40b747f414e280843adef658a75a98548af745ce139ce1a2ae2"},"schema_version":"1.0","source":{"id":"1812.05678","kind":"arxiv","version":5}},"canonical_sha256":"c126799ef6350c2a7804bd4d7d416d3276620a360826f7644defa5ae4259c376","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c126799ef6350c2a7804bd4d7d416d3276620a360826f7644defa5ae4259c376","first_computed_at":"2026-06-04T01:08:23.661755Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-04T01:08:23.661755Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IqJmS/wCKhkjT7GwOXSymzyheNhsysCKMsoOZgACnF7UEIYLngRsoQU4JUed8ifBflWVkeoENsxFWk3KsJ5vDg==","signature_status":"signed_v1","signed_at":"2026-06-04T01:08:23.662330Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.05678","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:be72b9451d35622b06d096446494b117603b37f8c517f76da96f38617b97a94c","sha256:67bab5c7f04e4304a8c72cfb5c44cc7a9f1448f2bb8b7772a54e5a4119fb094c"],"state_sha256":"58301e1a0e675255db2f000e595d2b2c860ff3468fe4cb62dd93b5137fe9133d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uQrwzRUYpoUmhGVLsy8Z9+xGwrnqpIlQkbnJC2dPK7wR6c5OQBq4d3O80s5BZC/imhW9azBQk2Z6UFPcewWIBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-21T01:33:45.637009Z","bundle_sha256":"707a0ffd9184411e2730a8e369354c16d1006f45fd3fdcecf8b2afc4b2f130c4"}}