{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:GFUZ3N3G256NSAYDGIHEPQLFY5","short_pith_number":"pith:GFUZ3N3G","canonical_record":{"source":{"id":"1802.06749","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-19T18:35:39Z","cross_cats_sorted":[],"title_canon_sha256":"cc7f3d447304023871ebc68b303f36248ab3b37faf4b1fcf150996b74d7bae99","abstract_canon_sha256":"ecf9357067a59013cdddd084cb7f5c2a93d9f75e288c047e28bd32757220bed3"},"schema_version":"1.0"},"canonical_sha256":"31699db766d77cd90303320e47c165c7581fde05480b18a083ded5f969166aa0","source":{"kind":"arxiv","id":"1802.06749","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.06749","created_at":"2026-05-18T00:06:30Z"},{"alias_kind":"arxiv_version","alias_value":"1802.06749v3","created_at":"2026-05-18T00:06:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.06749","created_at":"2026-05-18T00:06:30Z"},{"alias_kind":"pith_short_12","alias_value":"GFUZ3N3G256N","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"GFUZ3N3G256NSAYD","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"GFUZ3N3G","created_at":"2026-05-18T12:32:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:GFUZ3N3G256NSAYDGIHEPQLFY5","target":"record","payload":{"canonical_record":{"source":{"id":"1802.06749","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-19T18:35:39Z","cross_cats_sorted":[],"title_canon_sha256":"cc7f3d447304023871ebc68b303f36248ab3b37faf4b1fcf150996b74d7bae99","abstract_canon_sha256":"ecf9357067a59013cdddd084cb7f5c2a93d9f75e288c047e28bd32757220bed3"},"schema_version":"1.0"},"canonical_sha256":"31699db766d77cd90303320e47c165c7581fde05480b18a083ded5f969166aa0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:30.728899Z","signature_b64":"Rfi1ZKIPfK+FxOmHoqYCt+yqtluYNsKsNpub6E7D9VobE8jZeNnJ9RNtPwYki6V55ngg2UwHQR+trB24VJEvCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"31699db766d77cd90303320e47c165c7581fde05480b18a083ded5f969166aa0","last_reissued_at":"2026-05-18T00:06:30.728484Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:30.728484Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1802.06749","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:06:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SdtCdqkpolQ1dPlDMLru6A8UkTzTxOlk7nl7w2c1T/QrS18YakIBxC1AOAVMio/RXQlTY605wDRQ7KAPYqfpDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T23:55:54.556897Z"},"content_sha256":"66578cc045c1f24ed1c425e8279f282fc8793e1587f46936f941cf110fa218d5","schema_version":"1.0","event_id":"sha256:66578cc045c1f24ed1c425e8279f282fc8793e1587f46936f941cf110fa218d5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:GFUZ3N3G256NSAYDGIHEPQLFY5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Leveraged volume sampling for linear regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Daniel Hsu, Manfred K. Warmuth, Micha{\\l} Derezi\\'nski","submitted_at":"2018-02-19T18:35:39Z","abstract_excerpt":"Suppose an $n \\times d$ design matrix in a linear regression problem is given, but the response for each point is hidden unless explicitly requested. The goal is to sample only a small number $k \\ll n$ of the responses, and then produce a weight vector whose sum of squares loss over all points is at most $1+\\epsilon$ times the minimum. When $k$ is very small (e.g., $k=d$), jointly sampling diverse subsets of points is crucial. One such method called volume sampling has a unique and desirable property that the weight vector it produces is an unbiased estimate of the optimum. It is therefore nat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.06749","kind":"arxiv","version":3},"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":"2026-05-18T00:06:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cKujWe00I9Jrn4cuLqh4M2zjgcsg5+cEXijQ9qsmF9xFQBDEq7rCsnnvSwHV6FaPsMwODCZHIXBCUTos7zdWBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T23:55:54.557603Z"},"content_sha256":"222f51f020039fb2b767886956e85fe25d03a3e553a940bec4028677a0998bfb","schema_version":"1.0","event_id":"sha256:222f51f020039fb2b767886956e85fe25d03a3e553a940bec4028677a0998bfb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GFUZ3N3G256NSAYDGIHEPQLFY5/bundle.json","state_url":"https://pith.science/pith/GFUZ3N3G256NSAYDGIHEPQLFY5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GFUZ3N3G256NSAYDGIHEPQLFY5/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":"2026-06-07T23:55:54Z","links":{"resolver":"https://pith.science/pith/GFUZ3N3G256NSAYDGIHEPQLFY5","bundle":"https://pith.science/pith/GFUZ3N3G256NSAYDGIHEPQLFY5/bundle.json","state":"https://pith.science/pith/GFUZ3N3G256NSAYDGIHEPQLFY5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GFUZ3N3G256NSAYDGIHEPQLFY5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:GFUZ3N3G256NSAYDGIHEPQLFY5","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":"ecf9357067a59013cdddd084cb7f5c2a93d9f75e288c047e28bd32757220bed3","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-19T18:35:39Z","title_canon_sha256":"cc7f3d447304023871ebc68b303f36248ab3b37faf4b1fcf150996b74d7bae99"},"schema_version":"1.0","source":{"id":"1802.06749","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.06749","created_at":"2026-05-18T00:06:30Z"},{"alias_kind":"arxiv_version","alias_value":"1802.06749v3","created_at":"2026-05-18T00:06:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.06749","created_at":"2026-05-18T00:06:30Z"},{"alias_kind":"pith_short_12","alias_value":"GFUZ3N3G256N","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"GFUZ3N3G256NSAYD","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"GFUZ3N3G","created_at":"2026-05-18T12:32:25Z"}],"graph_snapshots":[{"event_id":"sha256:222f51f020039fb2b767886956e85fe25d03a3e553a940bec4028677a0998bfb","target":"graph","created_at":"2026-05-18T00:06:30Z","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":"Suppose an $n \\times d$ design matrix in a linear regression problem is given, but the response for each point is hidden unless explicitly requested. The goal is to sample only a small number $k \\ll n$ of the responses, and then produce a weight vector whose sum of squares loss over all points is at most $1+\\epsilon$ times the minimum. When $k$ is very small (e.g., $k=d$), jointly sampling diverse subsets of points is crucial. One such method called volume sampling has a unique and desirable property that the weight vector it produces is an unbiased estimate of the optimum. It is therefore nat","authors_text":"Daniel Hsu, Manfred K. Warmuth, Micha{\\l} Derezi\\'nski","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-19T18:35:39Z","title":"Leveraged volume sampling for linear regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.06749","kind":"arxiv","version":3},"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":"sha256:66578cc045c1f24ed1c425e8279f282fc8793e1587f46936f941cf110fa218d5","target":"record","created_at":"2026-05-18T00:06:30Z","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":"ecf9357067a59013cdddd084cb7f5c2a93d9f75e288c047e28bd32757220bed3","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-19T18:35:39Z","title_canon_sha256":"cc7f3d447304023871ebc68b303f36248ab3b37faf4b1fcf150996b74d7bae99"},"schema_version":"1.0","source":{"id":"1802.06749","kind":"arxiv","version":3}},"canonical_sha256":"31699db766d77cd90303320e47c165c7581fde05480b18a083ded5f969166aa0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"31699db766d77cd90303320e47c165c7581fde05480b18a083ded5f969166aa0","first_computed_at":"2026-05-18T00:06:30.728484Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:06:30.728484Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Rfi1ZKIPfK+FxOmHoqYCt+yqtluYNsKsNpub6E7D9VobE8jZeNnJ9RNtPwYki6V55ngg2UwHQR+trB24VJEvCA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:06:30.728899Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.06749","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:66578cc045c1f24ed1c425e8279f282fc8793e1587f46936f941cf110fa218d5","sha256:222f51f020039fb2b767886956e85fe25d03a3e553a940bec4028677a0998bfb"],"state_sha256":"2889c9b3a0940c17b12838ddf2c4cb8cc5f1882245b4127d026167ff1f2a8c8e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RuMP7B3OvSsw9h2G62wrKyo8OtRLlqPFq+BzbEmU5sAq+fDEwG2gRpNuxZkkbSQaPf2biP8PTordMaYpOJysAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T23:55:54.561049Z","bundle_sha256":"a029bc46ca87b01dc0da821aef70c16f12f9188b02f08ff1a4ece52532aa6479"}}