{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:YOMUHHAJ4LHNEBONOSWTBQLZLY","short_pith_number":"pith:YOMUHHAJ","canonical_record":{"source":{"id":"1608.08852","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-08-31T13:53:09Z","cross_cats_sorted":["cs.LG","math.ST","stat.TH"],"title_canon_sha256":"13a67db721938e11e47e548dab8a32e582dba5593bd6582c27434128d396e93a","abstract_canon_sha256":"354a39cfc3f7246355dad1420ab53508357ecd51260bd05838f1cbd566a1ca90"},"schema_version":"1.0"},"canonical_sha256":"c399439c09e2ced205cd74ad30c1795e0007abc769e3afa17c8d166356a83d54","source":{"kind":"arxiv","id":"1608.08852","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.08852","created_at":"2026-05-18T00:53:59Z"},{"alias_kind":"arxiv_version","alias_value":"1608.08852v1","created_at":"2026-05-18T00:53:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.08852","created_at":"2026-05-18T00:53:59Z"},{"alias_kind":"pith_short_12","alias_value":"YOMUHHAJ4LHN","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_16","alias_value":"YOMUHHAJ4LHNEBON","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_8","alias_value":"YOMUHHAJ","created_at":"2026-05-18T12:30:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:YOMUHHAJ4LHNEBONOSWTBQLZLY","target":"record","payload":{"canonical_record":{"source":{"id":"1608.08852","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-08-31T13:53:09Z","cross_cats_sorted":["cs.LG","math.ST","stat.TH"],"title_canon_sha256":"13a67db721938e11e47e548dab8a32e582dba5593bd6582c27434128d396e93a","abstract_canon_sha256":"354a39cfc3f7246355dad1420ab53508357ecd51260bd05838f1cbd566a1ca90"},"schema_version":"1.0"},"canonical_sha256":"c399439c09e2ced205cd74ad30c1795e0007abc769e3afa17c8d166356a83d54","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:53:59.062283Z","signature_b64":"cmzazK/FEQFvJMAnztqa19dfzD2rl/9w3wHcMMRLlQRmufN9x5MnuR1xeCQ57BEB4dtMNlZ+WItsYQoVvYTWCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c399439c09e2ced205cd74ad30c1795e0007abc769e3afa17c8d166356a83d54","last_reissued_at":"2026-05-18T00:53:59.061623Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:53:59.061623Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1608.08852","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":"2026-05-18T00:53:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uAgGy8SbVm3s4RO4QFkGYuPeeYWSc2ZTBP+r6nzF7iNAxyC7S4s++2w+68nYMleAdkoWkGbGt4kxmbGLdkZKCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T18:03:29.498873Z"},"content_sha256":"a9fabd8cbd49e488b62888971d40c29ba6b205ddcea9bdf2482a290c08302094","schema_version":"1.0","event_id":"sha256:a9fabd8cbd49e488b62888971d40c29ba6b205ddcea9bdf2482a290c08302094"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:YOMUHHAJ4LHNEBONOSWTBQLZLY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Mathematical Framework for Feature Selection from Real-World Data with Non-Linear Observations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.TH"],"primary_cat":"stat.ML","authors_text":"Gitta Kutyniok, Martin Genzel","submitted_at":"2016-08-31T13:53:09Z","abstract_excerpt":"In this paper, we study the challenge of feature selection based on a relatively small collection of sample pairs $\\{(x_i, y_i)\\}_{1 \\leq i \\leq m}$. The observations $y_i \\in \\mathbb{R}$ are thereby supposed to follow a noisy single-index model, depending on a certain set of signal variables. A major difficulty is that these variables usually cannot be observed directly, but rather arise as hidden factors in the actual data vectors $x_i \\in \\mathbb{R}^d$ (feature variables). We will prove that a successful variable selection is still possible in this setup, even when the applied estimator doe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.08852","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":"2026-05-18T00:53:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6qjCxXuYRYEzSo1A8ZO5lfoPbb/FjAaQXgIzVZPMc67HV3IFARAXdcquuU74Ij3uy9K48q2GySbuCudMW08NBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T18:03:29.499421Z"},"content_sha256":"b783f9ed4aec38cd6d109087ec093b5a01bb7ee82d8689201e4ee8d1c52fd1f7","schema_version":"1.0","event_id":"sha256:b783f9ed4aec38cd6d109087ec093b5a01bb7ee82d8689201e4ee8d1c52fd1f7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YOMUHHAJ4LHNEBONOSWTBQLZLY/bundle.json","state_url":"https://pith.science/pith/YOMUHHAJ4LHNEBONOSWTBQLZLY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YOMUHHAJ4LHNEBONOSWTBQLZLY/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":"2026-05-25T18:03:29Z","links":{"resolver":"https://pith.science/pith/YOMUHHAJ4LHNEBONOSWTBQLZLY","bundle":"https://pith.science/pith/YOMUHHAJ4LHNEBONOSWTBQLZLY/bundle.json","state":"https://pith.science/pith/YOMUHHAJ4LHNEBONOSWTBQLZLY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YOMUHHAJ4LHNEBONOSWTBQLZLY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:YOMUHHAJ4LHNEBONOSWTBQLZLY","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":"354a39cfc3f7246355dad1420ab53508357ecd51260bd05838f1cbd566a1ca90","cross_cats_sorted":["cs.LG","math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-08-31T13:53:09Z","title_canon_sha256":"13a67db721938e11e47e548dab8a32e582dba5593bd6582c27434128d396e93a"},"schema_version":"1.0","source":{"id":"1608.08852","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.08852","created_at":"2026-05-18T00:53:59Z"},{"alias_kind":"arxiv_version","alias_value":"1608.08852v1","created_at":"2026-05-18T00:53:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.08852","created_at":"2026-05-18T00:53:59Z"},{"alias_kind":"pith_short_12","alias_value":"YOMUHHAJ4LHN","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_16","alias_value":"YOMUHHAJ4LHNEBON","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_8","alias_value":"YOMUHHAJ","created_at":"2026-05-18T12:30:53Z"}],"graph_snapshots":[{"event_id":"sha256:b783f9ed4aec38cd6d109087ec093b5a01bb7ee82d8689201e4ee8d1c52fd1f7","target":"graph","created_at":"2026-05-18T00:53:59Z","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":"In this paper, we study the challenge of feature selection based on a relatively small collection of sample pairs $\\{(x_i, y_i)\\}_{1 \\leq i \\leq m}$. The observations $y_i \\in \\mathbb{R}$ are thereby supposed to follow a noisy single-index model, depending on a certain set of signal variables. A major difficulty is that these variables usually cannot be observed directly, but rather arise as hidden factors in the actual data vectors $x_i \\in \\mathbb{R}^d$ (feature variables). We will prove that a successful variable selection is still possible in this setup, even when the applied estimator doe","authors_text":"Gitta Kutyniok, Martin Genzel","cross_cats":["cs.LG","math.ST","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-08-31T13:53:09Z","title":"A Mathematical Framework for Feature Selection from Real-World Data with Non-Linear Observations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.08852","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:a9fabd8cbd49e488b62888971d40c29ba6b205ddcea9bdf2482a290c08302094","target":"record","created_at":"2026-05-18T00:53:59Z","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":"354a39cfc3f7246355dad1420ab53508357ecd51260bd05838f1cbd566a1ca90","cross_cats_sorted":["cs.LG","math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-08-31T13:53:09Z","title_canon_sha256":"13a67db721938e11e47e548dab8a32e582dba5593bd6582c27434128d396e93a"},"schema_version":"1.0","source":{"id":"1608.08852","kind":"arxiv","version":1}},"canonical_sha256":"c399439c09e2ced205cd74ad30c1795e0007abc769e3afa17c8d166356a83d54","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c399439c09e2ced205cd74ad30c1795e0007abc769e3afa17c8d166356a83d54","first_computed_at":"2026-05-18T00:53:59.061623Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:53:59.061623Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cmzazK/FEQFvJMAnztqa19dfzD2rl/9w3wHcMMRLlQRmufN9x5MnuR1xeCQ57BEB4dtMNlZ+WItsYQoVvYTWCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:53:59.062283Z","signed_message":"canonical_sha256_bytes"},"source_id":"1608.08852","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a9fabd8cbd49e488b62888971d40c29ba6b205ddcea9bdf2482a290c08302094","sha256:b783f9ed4aec38cd6d109087ec093b5a01bb7ee82d8689201e4ee8d1c52fd1f7"],"state_sha256":"70825b2b668c75476bdc1e726af3a8045d3d947250d4eeb643b7e88936b5658c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"C/flsuf4LqFp8/KPpwYNlhD/LAfMyd3fFX42rbU4sVQncQKx/PiHYua0MqzuNtiev4NBv9tCRk8vjIZL5uCKBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T18:03:29.503326Z","bundle_sha256":"0cea0534260129e01e188c4c4a6533fc711fa7a1ec386fa588150da6e9a465f8"}}