{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:WWSXHNSDA25ESCO53QKYRG2B4E","short_pith_number":"pith:WWSXHNSD","canonical_record":{"source":{"id":"1206.4678","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-06-18T15:37:23Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"7e34d679a13cf49ec3998a75d24c57cc2a346663b4e288e89bee7554d9d5b21c","abstract_canon_sha256":"7b454c23e5d767795e43ad856d56512d5f037f740edb9d1e7f550a5e5f265aff"},"schema_version":"1.0"},"canonical_sha256":"b5a573b64306ba4909dddc15889b41e12c2084f6cd5cb7991e7a956177c3e1ea","source":{"kind":"arxiv","id":"1206.4678","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1206.4678","created_at":"2026-05-18T03:53:04Z"},{"alias_kind":"arxiv_version","alias_value":"1206.4678v1","created_at":"2026-05-18T03:53:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1206.4678","created_at":"2026-05-18T03:53:04Z"},{"alias_kind":"pith_short_12","alias_value":"WWSXHNSDA25E","created_at":"2026-05-18T12:27:27Z"},{"alias_kind":"pith_short_16","alias_value":"WWSXHNSDA25ESCO5","created_at":"2026-05-18T12:27:27Z"},{"alias_kind":"pith_short_8","alias_value":"WWSXHNSD","created_at":"2026-05-18T12:27:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:WWSXHNSDA25ESCO53QKYRG2B4E","target":"record","payload":{"canonical_record":{"source":{"id":"1206.4678","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-06-18T15:37:23Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"7e34d679a13cf49ec3998a75d24c57cc2a346663b4e288e89bee7554d9d5b21c","abstract_canon_sha256":"7b454c23e5d767795e43ad856d56512d5f037f740edb9d1e7f550a5e5f265aff"},"schema_version":"1.0"},"canonical_sha256":"b5a573b64306ba4909dddc15889b41e12c2084f6cd5cb7991e7a956177c3e1ea","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:53:04.035920Z","signature_b64":"xaw0St+oezh4uzoQsbsqwdrabNJ9G7VEFERMHcnnG2yEs4/5YtvCCGAuEjH+NQI86Rhmk6FwSsgg4uKe7ZX7Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b5a573b64306ba4909dddc15889b41e12c2084f6cd5cb7991e7a956177c3e1ea","last_reissued_at":"2026-05-18T03:53:04.035190Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:53:04.035190Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1206.4678","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":"2026-05-18T03:53:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9aUcGrle8QUSPW3IOgdRebE0peGWepBEQunnlC2yPjxxHgSmsQCo7VX5Hfej893OGcv2QwDrc/NfSKAN+EirBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T21:46:25.432148Z"},"content_sha256":"df24f6c1ba0c0a1aed0b52b9486f0529c20631493bbc62a00ee514102409fa37","schema_version":"1.0","event_id":"sha256:df24f6c1ba0c0a1aed0b52b9486f0529c20631493bbc62a00ee514102409fa37"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:WWSXHNSDA25ESCO53QKYRG2B4E","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Linear Regression with Limited Observation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Elad Hazan (Technion), Tomer Koren (Technion)","submitted_at":"2012-06-18T15:37:23Z","abstract_excerpt":"We consider the most common variants of linear regression, including Ridge, Lasso and Support-vector regression, in a setting where the learner is allowed to observe only a fixed number of attributes of each example at training time. We present simple and efficient algorithms for these problems: for Lasso and Ridge regression they need the same total number of attributes (up to constants) as do full-information algorithms, for reaching a certain accuracy. For Support-vector regression, we require exponentially less attributes compared to the state of the art. By that, we resolve an open proble"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1206.4678","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":"2026-05-18T03:53:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ldyDx3sFaXjmIU8eQ3UC0LYOwrYIrQDR6O4SZLJYYljCReqrpwKJoAK03Qw9mAR4K65/apos3wdTaye7y+a8AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T21:46:25.432509Z"},"content_sha256":"737a7fc2c014f6a0f2dc640fcf41f51fd25a9ddba7b30759b4f0317873ae631a","schema_version":"1.0","event_id":"sha256:737a7fc2c014f6a0f2dc640fcf41f51fd25a9ddba7b30759b4f0317873ae631a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WWSXHNSDA25ESCO53QKYRG2B4E/bundle.json","state_url":"https://pith.science/pith/WWSXHNSDA25ESCO53QKYRG2B4E/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WWSXHNSDA25ESCO53QKYRG2B4E/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":"2026-08-05T21:46:25Z","links":{"resolver":"https://pith.science/pith/WWSXHNSDA25ESCO53QKYRG2B4E","bundle":"https://pith.science/pith/WWSXHNSDA25ESCO53QKYRG2B4E/bundle.json","state":"https://pith.science/pith/WWSXHNSDA25ESCO53QKYRG2B4E/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WWSXHNSDA25ESCO53QKYRG2B4E/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:WWSXHNSDA25ESCO53QKYRG2B4E","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":"7b454c23e5d767795e43ad856d56512d5f037f740edb9d1e7f550a5e5f265aff","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-06-18T15:37:23Z","title_canon_sha256":"7e34d679a13cf49ec3998a75d24c57cc2a346663b4e288e89bee7554d9d5b21c"},"schema_version":"1.0","source":{"id":"1206.4678","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1206.4678","created_at":"2026-05-18T03:53:04Z"},{"alias_kind":"arxiv_version","alias_value":"1206.4678v1","created_at":"2026-05-18T03:53:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1206.4678","created_at":"2026-05-18T03:53:04Z"},{"alias_kind":"pith_short_12","alias_value":"WWSXHNSDA25E","created_at":"2026-05-18T12:27:27Z"},{"alias_kind":"pith_short_16","alias_value":"WWSXHNSDA25ESCO5","created_at":"2026-05-18T12:27:27Z"},{"alias_kind":"pith_short_8","alias_value":"WWSXHNSD","created_at":"2026-05-18T12:27:27Z"}],"graph_snapshots":[{"event_id":"sha256:737a7fc2c014f6a0f2dc640fcf41f51fd25a9ddba7b30759b4f0317873ae631a","target":"graph","created_at":"2026-05-18T03:53:04Z","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":"We consider the most common variants of linear regression, including Ridge, Lasso and Support-vector regression, in a setting where the learner is allowed to observe only a fixed number of attributes of each example at training time. We present simple and efficient algorithms for these problems: for Lasso and Ridge regression they need the same total number of attributes (up to constants) as do full-information algorithms, for reaching a certain accuracy. For Support-vector regression, we require exponentially less attributes compared to the state of the art. By that, we resolve an open proble","authors_text":"Elad Hazan (Technion), Tomer Koren (Technion)","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-06-18T15:37:23Z","title":"Linear Regression with Limited Observation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1206.4678","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:df24f6c1ba0c0a1aed0b52b9486f0529c20631493bbc62a00ee514102409fa37","target":"record","created_at":"2026-05-18T03:53:04Z","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":"7b454c23e5d767795e43ad856d56512d5f037f740edb9d1e7f550a5e5f265aff","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-06-18T15:37:23Z","title_canon_sha256":"7e34d679a13cf49ec3998a75d24c57cc2a346663b4e288e89bee7554d9d5b21c"},"schema_version":"1.0","source":{"id":"1206.4678","kind":"arxiv","version":1}},"canonical_sha256":"b5a573b64306ba4909dddc15889b41e12c2084f6cd5cb7991e7a956177c3e1ea","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b5a573b64306ba4909dddc15889b41e12c2084f6cd5cb7991e7a956177c3e1ea","first_computed_at":"2026-05-18T03:53:04.035190Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:53:04.035190Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xaw0St+oezh4uzoQsbsqwdrabNJ9G7VEFERMHcnnG2yEs4/5YtvCCGAuEjH+NQI86Rhmk6FwSsgg4uKe7ZX7Dw==","signature_status":"signed_v1","signed_at":"2026-05-18T03:53:04.035920Z","signed_message":"canonical_sha256_bytes"},"source_id":"1206.4678","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:df24f6c1ba0c0a1aed0b52b9486f0529c20631493bbc62a00ee514102409fa37","sha256:737a7fc2c014f6a0f2dc640fcf41f51fd25a9ddba7b30759b4f0317873ae631a"],"state_sha256":"5b869465aa6181f011eb2dfc6439adb0cf399cbdb4c5849eda48fcf6909b298d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jC5gpo3qEq/HUAW/XK81QwxqKrH7JyO6EgJ6cEq6q06nuJt/H97Qyx/+sVOVGDLG0LQ1332ovfZs1wmGPxclCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T21:46:25.434779Z","bundle_sha256":"14a56962bd5b87258ff2282335655ee9b34b54c0572f306c6b00520d814abd99"}}