{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:L46KI5RXD2C5NXHVM6SB7TTQPV","short_pith_number":"pith:L46KI5RX","canonical_record":{"source":{"id":"2006.09862","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-17T13:42:09Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"8d0ea0419f6ea568bc7a96351c027d41d2b28bc260ef3cfc2a0ef5046b6da2bf","abstract_canon_sha256":"1e6a526dd7eb61fd34835a77873463de2fd8871ae75166ba5cdd9396dfc193a0"},"schema_version":"1.0"},"canonical_sha256":"5f3ca476371e85d6dcf567a41fce707d710a117fda4938fcb75e45542e297c62","source":{"kind":"arxiv","id":"2006.09862","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.09862","created_at":"2026-07-05T02:31:18Z"},{"alias_kind":"arxiv_version","alias_value":"2006.09862v2","created_at":"2026-07-05T02:31:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.09862","created_at":"2026-07-05T02:31:18Z"},{"alias_kind":"pith_short_12","alias_value":"L46KI5RXD2C5","created_at":"2026-07-05T02:31:18Z"},{"alias_kind":"pith_short_16","alias_value":"L46KI5RXD2C5NXHV","created_at":"2026-07-05T02:31:18Z"},{"alias_kind":"pith_short_8","alias_value":"L46KI5RX","created_at":"2026-07-05T02:31:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:L46KI5RXD2C5NXHVM6SB7TTQPV","target":"record","payload":{"canonical_record":{"source":{"id":"2006.09862","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-17T13:42:09Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"8d0ea0419f6ea568bc7a96351c027d41d2b28bc260ef3cfc2a0ef5046b6da2bf","abstract_canon_sha256":"1e6a526dd7eb61fd34835a77873463de2fd8871ae75166ba5cdd9396dfc193a0"},"schema_version":"1.0"},"canonical_sha256":"5f3ca476371e85d6dcf567a41fce707d710a117fda4938fcb75e45542e297c62","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:31:18.348281Z","signature_b64":"B4ZTfpQOnbUfMYRF6rhfyLVlAU2Ydrx6XeveSBeGrtl9JZ3FJW7SLiPsf3JeAuwuTBMu1Tkku/blWWE3JL87DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5f3ca476371e85d6dcf567a41fce707d710a117fda4938fcb75e45542e297c62","last_reissued_at":"2026-07-05T02:31:18.347869Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:31:18.347869Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2006.09862","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:31:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aKKMZDwjn7oNd2JDU9p4vI4B0I/uP/WFb6/ODlV9zYTEQq3TgDwGGVLtBsqaj157Pitk4NkRDpje/K2BIfJcBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T12:24:53.178503Z"},"content_sha256":"a50f8cfc9c83a3edea8b660b55cb32e7c7434f0cf60c0c56250afd0619c6c72f","schema_version":"1.0","event_id":"sha256:a50f8cfc9c83a3edea8b660b55cb32e7c7434f0cf60c0c56250afd0619c6c72f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:L46KI5RXD2C5NXHVM6SB7TTQPV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Elvis Dohmatob, Insu Han, Jennifer Gillenwater, Mike Gartrell, Victor-Emmanuel Brunel","submitted_at":"2020-06-17T13:42:09Z","abstract_excerpt":"Determinantal point processes (DPPs) have attracted significant attention in machine learning for their ability to model subsets drawn from a large item collection. Recent work shows that nonsymmetric DPP (NDPP) kernels have significant advantages over symmetric kernels in terms of modeling power and predictive performance. However, for an item collection of size $M$, existing NDPP learning and inference algorithms require memory quadratic in $M$ and runtime cubic (for learning) or quadratic (for inference) in $M$, making them impractical for many typical subset selection tasks. In this work, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.09862","kind":"arxiv","version":2},"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/2006.09862/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":"2026-07-05T02:31:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Zz5ufuYiVhqB7MOESTnuUUvNsma77rFDq18//bu2G76C5y7oiXexSrHRncJZitwdHCr2J5qyY5phO/kTvW+eBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T12:24:53.179238Z"},"content_sha256":"c457f98c12f66f0ab3edf30321e3438a488b2c9415e07af9c6f07801d305cace","schema_version":"1.0","event_id":"sha256:c457f98c12f66f0ab3edf30321e3438a488b2c9415e07af9c6f07801d305cace"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/L46KI5RXD2C5NXHVM6SB7TTQPV/bundle.json","state_url":"https://pith.science/pith/L46KI5RXD2C5NXHVM6SB7TTQPV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/L46KI5RXD2C5NXHVM6SB7TTQPV/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":"2026-08-12T12:24:53Z","links":{"resolver":"https://pith.science/pith/L46KI5RXD2C5NXHVM6SB7TTQPV","bundle":"https://pith.science/pith/L46KI5RXD2C5NXHVM6SB7TTQPV/bundle.json","state":"https://pith.science/pith/L46KI5RXD2C5NXHVM6SB7TTQPV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/L46KI5RXD2C5NXHVM6SB7TTQPV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:L46KI5RXD2C5NXHVM6SB7TTQPV","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":"1e6a526dd7eb61fd34835a77873463de2fd8871ae75166ba5cdd9396dfc193a0","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-17T13:42:09Z","title_canon_sha256":"8d0ea0419f6ea568bc7a96351c027d41d2b28bc260ef3cfc2a0ef5046b6da2bf"},"schema_version":"1.0","source":{"id":"2006.09862","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.09862","created_at":"2026-07-05T02:31:18Z"},{"alias_kind":"arxiv_version","alias_value":"2006.09862v2","created_at":"2026-07-05T02:31:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.09862","created_at":"2026-07-05T02:31:18Z"},{"alias_kind":"pith_short_12","alias_value":"L46KI5RXD2C5","created_at":"2026-07-05T02:31:18Z"},{"alias_kind":"pith_short_16","alias_value":"L46KI5RXD2C5NXHV","created_at":"2026-07-05T02:31:18Z"},{"alias_kind":"pith_short_8","alias_value":"L46KI5RX","created_at":"2026-07-05T02:31:18Z"}],"graph_snapshots":[{"event_id":"sha256:c457f98c12f66f0ab3edf30321e3438a488b2c9415e07af9c6f07801d305cace","target":"graph","created_at":"2026-07-05T02:31:18Z","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/2006.09862/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Determinantal point processes (DPPs) have attracted significant attention in machine learning for their ability to model subsets drawn from a large item collection. Recent work shows that nonsymmetric DPP (NDPP) kernels have significant advantages over symmetric kernels in terms of modeling power and predictive performance. However, for an item collection of size $M$, existing NDPP learning and inference algorithms require memory quadratic in $M$ and runtime cubic (for learning) or quadratic (for inference) in $M$, making them impractical for many typical subset selection tasks. In this work, ","authors_text":"Elvis Dohmatob, Insu Han, Jennifer Gillenwater, Mike Gartrell, Victor-Emmanuel Brunel","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-17T13:42:09Z","title":"Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.09862","kind":"arxiv","version":2},"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:a50f8cfc9c83a3edea8b660b55cb32e7c7434f0cf60c0c56250afd0619c6c72f","target":"record","created_at":"2026-07-05T02:31:18Z","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":"1e6a526dd7eb61fd34835a77873463de2fd8871ae75166ba5cdd9396dfc193a0","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-17T13:42:09Z","title_canon_sha256":"8d0ea0419f6ea568bc7a96351c027d41d2b28bc260ef3cfc2a0ef5046b6da2bf"},"schema_version":"1.0","source":{"id":"2006.09862","kind":"arxiv","version":2}},"canonical_sha256":"5f3ca476371e85d6dcf567a41fce707d710a117fda4938fcb75e45542e297c62","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5f3ca476371e85d6dcf567a41fce707d710a117fda4938fcb75e45542e297c62","first_computed_at":"2026-07-05T02:31:18.347869Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:31:18.347869Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"B4ZTfpQOnbUfMYRF6rhfyLVlAU2Ydrx6XeveSBeGrtl9JZ3FJW7SLiPsf3JeAuwuTBMu1Tkku/blWWE3JL87DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:31:18.348281Z","signed_message":"canonical_sha256_bytes"},"source_id":"2006.09862","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a50f8cfc9c83a3edea8b660b55cb32e7c7434f0cf60c0c56250afd0619c6c72f","sha256:c457f98c12f66f0ab3edf30321e3438a488b2c9415e07af9c6f07801d305cace"],"state_sha256":"74511f568a451768034bb4b5e1416793180bbe522caac5044bb05d5bc591986f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tCzdV1opcJDlcrFy50z0FAz5r0FDuHIAbObNtFRI7JX6QJlZUaFkAhXCaoe/fsVyM6wr+fk21teNR2rODFbmBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T12:24:53.190091Z","bundle_sha256":"9f5044e7d72f39fb5c6bed8112ebc36bd53f333f85a48e41b4c20fac69ce339d"}}