{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:LEECP4TMECHJIHE6M24ADY7OKM","short_pith_number":"pith:LEECP4TM","schema_version":"1.0","canonical_sha256":"590827f26c208e941c9e66b801e3ee531e5cc07e647ef941a6386acef1d29f3b","source":{"kind":"arxiv","id":"1812.06360","version":1},"attestation_state":"computed","paper":{"title":"A Bandit Approach to Maximum Inner Product Search","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Barzan Mozafari, Rui Liu, Tianyi Wu","submitted_at":"2018-12-15T21:33:56Z","abstract_excerpt":"There has been substantial research on sub-linear time approximate algorithms for Maximum Inner Product Search (MIPS). To achieve fast query time, state-of-the-art techniques require significant preprocessing, which can be a burden when the number of subsequent queries is not sufficiently large to amortize the cost. Furthermore, existing methods do not have the ability to directly control the suboptimality of their approximate results with theoretical guarantees. In this paper, we propose the first approximate algorithm for MIPS that does not require any preprocessing, and allows users to cont"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1812.06360","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-15T21:33:56Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"65038bbdfc26d01836bf7d7af97f307220a4b42359a0b9d5ed5e32e6e30361d2","abstract_canon_sha256":"c9664ed08c4b76387f3160b18e7af684eca648e57f95ac57c4022b11258e4fb3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:10.443724Z","signature_b64":"Z/JQXCqCucVRmJrLqQi+HWELyoG4+XqlS7iLND+dfGlQwAdA2fbs7dDUhDCpqvFXiPnoSgpubyUz1C/7KhB1BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"590827f26c208e941c9e66b801e3ee531e5cc07e647ef941a6386acef1d29f3b","last_reissued_at":"2026-05-17T23:58:10.443226Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:10.443226Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Bandit Approach to Maximum Inner Product Search","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Barzan Mozafari, Rui Liu, Tianyi Wu","submitted_at":"2018-12-15T21:33:56Z","abstract_excerpt":"There has been substantial research on sub-linear time approximate algorithms for Maximum Inner Product Search (MIPS). To achieve fast query time, state-of-the-art techniques require significant preprocessing, which can be a burden when the number of subsequent queries is not sufficiently large to amortize the cost. Furthermore, existing methods do not have the ability to directly control the suboptimality of their approximate results with theoretical guarantees. In this paper, we propose the first approximate algorithm for MIPS that does not require any preprocessing, and allows users to cont"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.06360","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1812.06360","created_at":"2026-05-17T23:58:10.443307+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.06360v1","created_at":"2026-05-17T23:58:10.443307+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.06360","created_at":"2026-05-17T23:58:10.443307+00:00"},{"alias_kind":"pith_short_12","alias_value":"LEECP4TMECHJ","created_at":"2026-05-18T12:32:37.024351+00:00"},{"alias_kind":"pith_short_16","alias_value":"LEECP4TMECHJIHE6","created_at":"2026-05-18T12:32:37.024351+00:00"},{"alias_kind":"pith_short_8","alias_value":"LEECP4TM","created_at":"2026-05-18T12:32:37.024351+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LEECP4TMECHJIHE6M24ADY7OKM","json":"https://pith.science/pith/LEECP4TMECHJIHE6M24ADY7OKM.json","graph_json":"https://pith.science/api/pith-number/LEECP4TMECHJIHE6M24ADY7OKM/graph.json","events_json":"https://pith.science/api/pith-number/LEECP4TMECHJIHE6M24ADY7OKM/events.json","paper":"https://pith.science/paper/LEECP4TM"},"agent_actions":{"view_html":"https://pith.science/pith/LEECP4TMECHJIHE6M24ADY7OKM","download_json":"https://pith.science/pith/LEECP4TMECHJIHE6M24ADY7OKM.json","view_paper":"https://pith.science/paper/LEECP4TM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.06360&json=true","fetch_graph":"https://pith.science/api/pith-number/LEECP4TMECHJIHE6M24ADY7OKM/graph.json","fetch_events":"https://pith.science/api/pith-number/LEECP4TMECHJIHE6M24ADY7OKM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LEECP4TMECHJIHE6M24ADY7OKM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LEECP4TMECHJIHE6M24ADY7OKM/action/storage_attestation","attest_author":"https://pith.science/pith/LEECP4TMECHJIHE6M24ADY7OKM/action/author_attestation","sign_citation":"https://pith.science/pith/LEECP4TMECHJIHE6M24ADY7OKM/action/citation_signature","submit_replication":"https://pith.science/pith/LEECP4TMECHJIHE6M24ADY7OKM/action/replication_record"}},"created_at":"2026-05-17T23:58:10.443307+00:00","updated_at":"2026-05-17T23:58:10.443307+00:00"}