{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:H2RZU3JXEWBR2LDGNBIMSOP4ML","short_pith_number":"pith:H2RZU3JX","canonical_record":{"source":{"id":"2207.09560","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-07-19T21:28:51Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"8ef0d8067d8e3cfce9accc01c55690dec3ae3305df5238d79a874ea07003fcf9","abstract_canon_sha256":"1fe78126e515f2d568c4fb23cd3f91b7bed91ce23c69d9e23c06b04b1d5b87e3"},"schema_version":"1.0"},"canonical_sha256":"3ea39a6d3725831d2c666850c939fc62e0320c12d2e4ef6a99b0a0d00cd3e7c1","source":{"kind":"arxiv","id":"2207.09560","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.09560","created_at":"2026-07-05T10:08:10Z"},{"alias_kind":"arxiv_version","alias_value":"2207.09560v4","created_at":"2026-07-05T10:08:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.09560","created_at":"2026-07-05T10:08:10Z"},{"alias_kind":"pith_short_12","alias_value":"H2RZU3JXEWBR","created_at":"2026-07-05T10:08:10Z"},{"alias_kind":"pith_short_16","alias_value":"H2RZU3JXEWBR2LDG","created_at":"2026-07-05T10:08:10Z"},{"alias_kind":"pith_short_8","alias_value":"H2RZU3JX","created_at":"2026-07-05T10:08:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:H2RZU3JXEWBR2LDGNBIMSOP4ML","target":"record","payload":{"canonical_record":{"source":{"id":"2207.09560","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-07-19T21:28:51Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"8ef0d8067d8e3cfce9accc01c55690dec3ae3305df5238d79a874ea07003fcf9","abstract_canon_sha256":"1fe78126e515f2d568c4fb23cd3f91b7bed91ce23c69d9e23c06b04b1d5b87e3"},"schema_version":"1.0"},"canonical_sha256":"3ea39a6d3725831d2c666850c939fc62e0320c12d2e4ef6a99b0a0d00cd3e7c1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:08:10.242862Z","signature_b64":"cYLvCxNVoOdGATKAweXt0yojWSLB5ubLXxhG4VHTpajrojC2frvikBlWGjoVFUs5r++5EqkaKznPLfQt6UNTAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3ea39a6d3725831d2c666850c939fc62e0320c12d2e4ef6a99b0a0d00cd3e7c1","last_reissued_at":"2026-07-05T10:08:10.242513Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:08:10.242513Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2207.09560","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:08:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DWDn+3ORMPUCUT4D1CtOYf0/iGo7va71kxr8L0dR5+C9oCzQyWb1Iv0E26n0++MJGDA7J10Of8sk3YRAAOaYAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T18:21:53.166072Z"},"content_sha256":"b21a640466b8598f524f7c92b80b96b1c598452f4d7f3fd7e3d8678bad2b63aa","schema_version":"1.0","event_id":"sha256:b21a640466b8598f524f7c92b80b96b1c598452f4d7f3fd7e3d8678bad2b63aa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:H2RZU3JXEWBR2LDGNBIMSOP4ML","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Holistic Robust Data-Driven Decisions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Amine Bennouna, Bart Van Parys, Ryan Lucas","submitted_at":"2022-07-19T21:28:51Z","abstract_excerpt":"The design of data-driven formulations for machine learning and decision-making with good out-of-sample performance is a key challenge. The observation that good in-sample performance does not guarantee good out-of-sample performance is generally known as overfitting. Practical overfitting can typically not be attributed to a single cause but is caused by several factors simultaneously. We consider here three overfitting sources: (i) statistical error as a result of working with finite sample data, (ii) data noise, which occurs when the data points are measured only with finite precision, and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.09560","kind":"arxiv","version":4},"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/2207.09560/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":"2026-07-05T10:08:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Tki4LXJDqMAAMfOshAss7TX3PuoI9lmVb+tQ9TnQtGiKtQI1ifSya/6JbKNBU8v5YlT8a+liDzhNylV2+9AwAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T18:21:53.166567Z"},"content_sha256":"675fd53546fb356e6e09ca5ccbe9f3ad2ab4b558dc325c6ef309015965ca8e2b","schema_version":"1.0","event_id":"sha256:675fd53546fb356e6e09ca5ccbe9f3ad2ab4b558dc325c6ef309015965ca8e2b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/H2RZU3JXEWBR2LDGNBIMSOP4ML/bundle.json","state_url":"https://pith.science/pith/H2RZU3JXEWBR2LDGNBIMSOP4ML/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/H2RZU3JXEWBR2LDGNBIMSOP4ML/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":"2026-08-08T18:21:53Z","links":{"resolver":"https://pith.science/pith/H2RZU3JXEWBR2LDGNBIMSOP4ML","bundle":"https://pith.science/pith/H2RZU3JXEWBR2LDGNBIMSOP4ML/bundle.json","state":"https://pith.science/pith/H2RZU3JXEWBR2LDGNBIMSOP4ML/state.json","well_known_bundle":"https://pith.science/.well-known/pith/H2RZU3JXEWBR2LDGNBIMSOP4ML/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:H2RZU3JXEWBR2LDGNBIMSOP4ML","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":"1fe78126e515f2d568c4fb23cd3f91b7bed91ce23c69d9e23c06b04b1d5b87e3","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-07-19T21:28:51Z","title_canon_sha256":"8ef0d8067d8e3cfce9accc01c55690dec3ae3305df5238d79a874ea07003fcf9"},"schema_version":"1.0","source":{"id":"2207.09560","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.09560","created_at":"2026-07-05T10:08:10Z"},{"alias_kind":"arxiv_version","alias_value":"2207.09560v4","created_at":"2026-07-05T10:08:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.09560","created_at":"2026-07-05T10:08:10Z"},{"alias_kind":"pith_short_12","alias_value":"H2RZU3JXEWBR","created_at":"2026-07-05T10:08:10Z"},{"alias_kind":"pith_short_16","alias_value":"H2RZU3JXEWBR2LDG","created_at":"2026-07-05T10:08:10Z"},{"alias_kind":"pith_short_8","alias_value":"H2RZU3JX","created_at":"2026-07-05T10:08:10Z"}],"graph_snapshots":[{"event_id":"sha256:675fd53546fb356e6e09ca5ccbe9f3ad2ab4b558dc325c6ef309015965ca8e2b","target":"graph","created_at":"2026-07-05T10:08:10Z","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/2207.09560/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The design of data-driven formulations for machine learning and decision-making with good out-of-sample performance is a key challenge. The observation that good in-sample performance does not guarantee good out-of-sample performance is generally known as overfitting. Practical overfitting can typically not be attributed to a single cause but is caused by several factors simultaneously. We consider here three overfitting sources: (i) statistical error as a result of working with finite sample data, (ii) data noise, which occurs when the data points are measured only with finite precision, and ","authors_text":"Amine Bennouna, Bart Van Parys, Ryan Lucas","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-07-19T21:28:51Z","title":"Holistic Robust Data-Driven Decisions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.09560","kind":"arxiv","version":4},"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:b21a640466b8598f524f7c92b80b96b1c598452f4d7f3fd7e3d8678bad2b63aa","target":"record","created_at":"2026-07-05T10:08:10Z","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":"1fe78126e515f2d568c4fb23cd3f91b7bed91ce23c69d9e23c06b04b1d5b87e3","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-07-19T21:28:51Z","title_canon_sha256":"8ef0d8067d8e3cfce9accc01c55690dec3ae3305df5238d79a874ea07003fcf9"},"schema_version":"1.0","source":{"id":"2207.09560","kind":"arxiv","version":4}},"canonical_sha256":"3ea39a6d3725831d2c666850c939fc62e0320c12d2e4ef6a99b0a0d00cd3e7c1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3ea39a6d3725831d2c666850c939fc62e0320c12d2e4ef6a99b0a0d00cd3e7c1","first_computed_at":"2026-07-05T10:08:10.242513Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:08:10.242513Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cYLvCxNVoOdGATKAweXt0yojWSLB5ubLXxhG4VHTpajrojC2frvikBlWGjoVFUs5r++5EqkaKznPLfQt6UNTAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:08:10.242862Z","signed_message":"canonical_sha256_bytes"},"source_id":"2207.09560","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b21a640466b8598f524f7c92b80b96b1c598452f4d7f3fd7e3d8678bad2b63aa","sha256:675fd53546fb356e6e09ca5ccbe9f3ad2ab4b558dc325c6ef309015965ca8e2b"],"state_sha256":"973ce09a38e2b25e6719082fd8cb473c005480dae9c4b7f12134084866d40b15"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pD/19RQfzhVcZvGE1fVcmxMEIGyD9xTmurpp3/RsfxpsIkXVG963HAA2L81u02mmfv8kkVDS66+c70W6KkujCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T18:21:53.170529Z","bundle_sha256":"ddeb8811337291e6cfe3fe53f1105d74ca7593f5bce5bcdcbc8b60d934ddb20b"}}