{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:ISM33S2MIJRLQ7AQBQBVHRHOMV","short_pith_number":"pith:ISM33S2M","canonical_record":{"source":{"id":"2303.04301","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2023-03-08T00:43:06Z","cross_cats_sorted":["cs.DS","cs.LG"],"title_canon_sha256":"3b84da1ba29fd176ac169985ae96e6d97810ac164facf7c0711ed412395b596f","abstract_canon_sha256":"950e4b4cd14505cab14ae3c85e20a68110bab986f8760b8e25cb61030194bbd8"},"schema_version":"1.0"},"canonical_sha256":"4499bdcb4c4262b87c100c0353c4ee6561d195066a6db6ea2606a5b9d00fb049","source":{"kind":"arxiv","id":"2303.04301","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.04301","created_at":"2026-07-05T05:49:19Z"},{"alias_kind":"arxiv_version","alias_value":"2303.04301v1","created_at":"2026-07-05T05:49:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.04301","created_at":"2026-07-05T05:49:19Z"},{"alias_kind":"pith_short_12","alias_value":"ISM33S2MIJRL","created_at":"2026-07-05T05:49:19Z"},{"alias_kind":"pith_short_16","alias_value":"ISM33S2MIJRLQ7AQ","created_at":"2026-07-05T05:49:19Z"},{"alias_kind":"pith_short_8","alias_value":"ISM33S2M","created_at":"2026-07-05T05:49:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:ISM33S2MIJRLQ7AQBQBVHRHOMV","target":"record","payload":{"canonical_record":{"source":{"id":"2303.04301","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2023-03-08T00:43:06Z","cross_cats_sorted":["cs.DS","cs.LG"],"title_canon_sha256":"3b84da1ba29fd176ac169985ae96e6d97810ac164facf7c0711ed412395b596f","abstract_canon_sha256":"950e4b4cd14505cab14ae3c85e20a68110bab986f8760b8e25cb61030194bbd8"},"schema_version":"1.0"},"canonical_sha256":"4499bdcb4c4262b87c100c0353c4ee6561d195066a6db6ea2606a5b9d00fb049","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:49:19.619755Z","signature_b64":"NePm7AUAgsxeBOTb5ZSJn0WivlIz73uLK2RSo/wb30ACvmgBL2mSG8fypczHlzpXgt1Nji+A7/4P4uzwrmsJAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4499bdcb4c4262b87c100c0353c4ee6561d195066a6db6ea2606a5b9d00fb049","last_reissued_at":"2026-07-05T05:49:19.619234Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:49:19.619234Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2303.04301","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":"2026-07-05T05:49:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DOJCOVvAnrOmVirij7tjq/puxIKrlibLF+oO9WDilNDXtksHS5gPHr98OHiFlzeyw7BsQIF+cBRwucXR/rYqCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T20:43:59.507875Z"},"content_sha256":"375048c0a574e974156966adfcae9039f8f4992a73533b7de99d46b9cd922aa0","schema_version":"1.0","event_id":"sha256:375048c0a574e974156966adfcae9039f8f4992a73533b7de99d46b9cd922aa0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:ISM33S2MIJRLQ7AQBQBVHRHOMV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Optimal Sparse Recovery with Decision Stumps","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS","cs.LG"],"primary_cat":"stat.ML","authors_text":"Kiarash Banihashem, Max Springer, MohammadTaghi Hajiaghayi","submitted_at":"2023-03-08T00:43:06Z","abstract_excerpt":"Decision trees are widely used for their low computational cost, good predictive performance, and ability to assess the importance of features. Though often used in practice for feature selection, the theoretical guarantees of these methods are not well understood. We here obtain a tight finite sample bound for the feature selection problem in linear regression using single-depth decision trees. We examine the statistical properties of these \"decision stumps\" for the recovery of the $s$ active features from $p$ total features, where $s \\ll p$. Our analysis provides tight sample performance gua"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.04301","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2303.04301/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":"2026-07-05T05:49:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dv8Z5OcSHzxVdvfxOGzfPD2uHOnXU4bWYmUkJONsea6AMMJLNHvcE3lpUIOGNMvQxqLPJF6tqfceYjOKRV+jAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T20:43:59.508373Z"},"content_sha256":"82ae99e3897b34e19fe2bf2902dd983471ec5e580704fd42d7d815380248f59e","schema_version":"1.0","event_id":"sha256:82ae99e3897b34e19fe2bf2902dd983471ec5e580704fd42d7d815380248f59e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ISM33S2MIJRLQ7AQBQBVHRHOMV/bundle.json","state_url":"https://pith.science/pith/ISM33S2MIJRLQ7AQBQBVHRHOMV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ISM33S2MIJRLQ7AQBQBVHRHOMV/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":"2026-08-03T20:43:59Z","links":{"resolver":"https://pith.science/pith/ISM33S2MIJRLQ7AQBQBVHRHOMV","bundle":"https://pith.science/pith/ISM33S2MIJRLQ7AQBQBVHRHOMV/bundle.json","state":"https://pith.science/pith/ISM33S2MIJRLQ7AQBQBVHRHOMV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ISM33S2MIJRLQ7AQBQBVHRHOMV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:ISM33S2MIJRLQ7AQBQBVHRHOMV","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":"950e4b4cd14505cab14ae3c85e20a68110bab986f8760b8e25cb61030194bbd8","cross_cats_sorted":["cs.DS","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2023-03-08T00:43:06Z","title_canon_sha256":"3b84da1ba29fd176ac169985ae96e6d97810ac164facf7c0711ed412395b596f"},"schema_version":"1.0","source":{"id":"2303.04301","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.04301","created_at":"2026-07-05T05:49:19Z"},{"alias_kind":"arxiv_version","alias_value":"2303.04301v1","created_at":"2026-07-05T05:49:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.04301","created_at":"2026-07-05T05:49:19Z"},{"alias_kind":"pith_short_12","alias_value":"ISM33S2MIJRL","created_at":"2026-07-05T05:49:19Z"},{"alias_kind":"pith_short_16","alias_value":"ISM33S2MIJRLQ7AQ","created_at":"2026-07-05T05:49:19Z"},{"alias_kind":"pith_short_8","alias_value":"ISM33S2M","created_at":"2026-07-05T05:49:19Z"}],"graph_snapshots":[{"event_id":"sha256:82ae99e3897b34e19fe2bf2902dd983471ec5e580704fd42d7d815380248f59e","target":"graph","created_at":"2026-07-05T05:49:19Z","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/2303.04301/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Decision trees are widely used for their low computational cost, good predictive performance, and ability to assess the importance of features. Though often used in practice for feature selection, the theoretical guarantees of these methods are not well understood. We here obtain a tight finite sample bound for the feature selection problem in linear regression using single-depth decision trees. We examine the statistical properties of these \"decision stumps\" for the recovery of the $s$ active features from $p$ total features, where $s \\ll p$. Our analysis provides tight sample performance gua","authors_text":"Kiarash Banihashem, Max Springer, MohammadTaghi Hajiaghayi","cross_cats":["cs.DS","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2023-03-08T00:43:06Z","title":"Optimal Sparse Recovery with Decision Stumps"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.04301","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":"sha256:375048c0a574e974156966adfcae9039f8f4992a73533b7de99d46b9cd922aa0","target":"record","created_at":"2026-07-05T05:49:19Z","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":"950e4b4cd14505cab14ae3c85e20a68110bab986f8760b8e25cb61030194bbd8","cross_cats_sorted":["cs.DS","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2023-03-08T00:43:06Z","title_canon_sha256":"3b84da1ba29fd176ac169985ae96e6d97810ac164facf7c0711ed412395b596f"},"schema_version":"1.0","source":{"id":"2303.04301","kind":"arxiv","version":1}},"canonical_sha256":"4499bdcb4c4262b87c100c0353c4ee6561d195066a6db6ea2606a5b9d00fb049","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4499bdcb4c4262b87c100c0353c4ee6561d195066a6db6ea2606a5b9d00fb049","first_computed_at":"2026-07-05T05:49:19.619234Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:49:19.619234Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NePm7AUAgsxeBOTb5ZSJn0WivlIz73uLK2RSo/wb30ACvmgBL2mSG8fypczHlzpXgt1Nji+A7/4P4uzwrmsJAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:49:19.619755Z","signed_message":"canonical_sha256_bytes"},"source_id":"2303.04301","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:375048c0a574e974156966adfcae9039f8f4992a73533b7de99d46b9cd922aa0","sha256:82ae99e3897b34e19fe2bf2902dd983471ec5e580704fd42d7d815380248f59e"],"state_sha256":"004ac62a7c1654e5f3aba92965529b0c76767a18a7dcfe4747ba4e8a932b1f0b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NJZHYHN650BLH/HTYvWQNLzjM916/bqp+XM+mLNQFNRf/bzcU/mtMn4/Zj4pFlDivsF3pVDAUMrvPErthhc8BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T20:43:59.512899Z","bundle_sha256":"1ef0b2d83661c8ac70349fb054109ff8d5821fef1d60f3c467dc7d5ca7ce7539"}}