{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:LRTQL5CAVN7XMP2NKQLR2YV5NU","short_pith_number":"pith:LRTQL5CA","canonical_record":{"source":{"id":"1611.09328","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2016-11-28T20:33:15Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"c7c7904f14dd335bff33faa361c1464af033cc37625d79c501f1ddf7078d09dd","abstract_canon_sha256":"b1d887ab80d44bd84b1e60df32af2284bd0d968389c0ce486018166e4b7b4ce6"},"schema_version":"1.0"},"canonical_sha256":"5c6705f440ab7f763f4d54171d62bd6d259d664b7763d9709e7831b5f48ae218","source":{"kind":"arxiv","id":"1611.09328","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.09328","created_at":"2026-05-18T00:48:59Z"},{"alias_kind":"arxiv_version","alias_value":"1611.09328v2","created_at":"2026-05-18T00:48:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.09328","created_at":"2026-05-18T00:48:59Z"},{"alias_kind":"pith_short_12","alias_value":"LRTQL5CAVN7X","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_16","alias_value":"LRTQL5CAVN7XMP2N","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_8","alias_value":"LRTQL5CA","created_at":"2026-05-18T12:30:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:LRTQL5CAVN7XMP2NKQLR2YV5NU","target":"record","payload":{"canonical_record":{"source":{"id":"1611.09328","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2016-11-28T20:33:15Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"c7c7904f14dd335bff33faa361c1464af033cc37625d79c501f1ddf7078d09dd","abstract_canon_sha256":"b1d887ab80d44bd84b1e60df32af2284bd0d968389c0ce486018166e4b7b4ce6"},"schema_version":"1.0"},"canonical_sha256":"5c6705f440ab7f763f4d54171d62bd6d259d664b7763d9709e7831b5f48ae218","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:48:59.621622Z","signature_b64":"oqLiF4OOZRRQTK5Wa8nmzvgLRj6wpXewt/CH74Q3x9iixh3lM3RZQLwL/7TECUX/34tcYSVPnZkk5sgXF706AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5c6705f440ab7f763f4d54171d62bd6d259d664b7763d9709e7831b5f48ae218","last_reissued_at":"2026-05-18T00:48:59.620960Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:48:59.620960Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1611.09328","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":"2026-05-18T00:48:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+jiyxHGLEuh4gcY95/6YMV30oKOoac+HrT37BU5lcDn29/MPQ9UDWUH2DY7lLHEk9zOIGWKVxqHuROGyffrjCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T07:57:32.547996Z"},"content_sha256":"d3e6ecadf7610bbb36e4501a19641ed3bea5918a6d6bca95df3927f232b2a8af","schema_version":"1.0","event_id":"sha256:d3e6ecadf7610bbb36e4501a19641ed3bea5918a6d6bca95df3927f232b2a8af"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:LRTQL5CAVN7XMP2NKQLR2YV5NU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Accelerated Gradient Temporal Difference Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.AI","authors_text":"Adam White, Martha White, Yangchen Pan","submitted_at":"2016-11-28T20:33:15Z","abstract_excerpt":"The family of temporal difference (TD) methods span a spectrum from computationally frugal linear methods like TD({\\lambda}) to data efficient least squares methods. Least square methods make the best use of available data directly computing the TD solution and thus do not require tuning a typically highly sensitive learning rate parameter, but require quadratic computation and storage. Recent algorithmic developments have yielded several sub-quadratic methods that use an approximation to the least squares TD solution, but incur bias. In this paper, we propose a new family of accelerated gradi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.09328","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":""},"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":"2026-05-18T00:48:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MA8LkY5bcaEbRF8jKcQyfXdtLBOHOdZLiX7w6FfBpXz+I+K3GhcdodYg4y3Luqw9EgSBCvetll5ncGgpIhJ9DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T07:57:32.548732Z"},"content_sha256":"630f9a94a1b280d93c0e42e70d4a8efcbdae10194a97cf364df399c18ce9363f","schema_version":"1.0","event_id":"sha256:630f9a94a1b280d93c0e42e70d4a8efcbdae10194a97cf364df399c18ce9363f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LRTQL5CAVN7XMP2NKQLR2YV5NU/bundle.json","state_url":"https://pith.science/pith/LRTQL5CAVN7XMP2NKQLR2YV5NU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LRTQL5CAVN7XMP2NKQLR2YV5NU/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":"2026-05-25T07:57:32Z","links":{"resolver":"https://pith.science/pith/LRTQL5CAVN7XMP2NKQLR2YV5NU","bundle":"https://pith.science/pith/LRTQL5CAVN7XMP2NKQLR2YV5NU/bundle.json","state":"https://pith.science/pith/LRTQL5CAVN7XMP2NKQLR2YV5NU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LRTQL5CAVN7XMP2NKQLR2YV5NU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:LRTQL5CAVN7XMP2NKQLR2YV5NU","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":"b1d887ab80d44bd84b1e60df32af2284bd0d968389c0ce486018166e4b7b4ce6","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2016-11-28T20:33:15Z","title_canon_sha256":"c7c7904f14dd335bff33faa361c1464af033cc37625d79c501f1ddf7078d09dd"},"schema_version":"1.0","source":{"id":"1611.09328","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.09328","created_at":"2026-05-18T00:48:59Z"},{"alias_kind":"arxiv_version","alias_value":"1611.09328v2","created_at":"2026-05-18T00:48:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.09328","created_at":"2026-05-18T00:48:59Z"},{"alias_kind":"pith_short_12","alias_value":"LRTQL5CAVN7X","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_16","alias_value":"LRTQL5CAVN7XMP2N","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_8","alias_value":"LRTQL5CA","created_at":"2026-05-18T12:30:29Z"}],"graph_snapshots":[{"event_id":"sha256:630f9a94a1b280d93c0e42e70d4a8efcbdae10194a97cf364df399c18ce9363f","target":"graph","created_at":"2026-05-18T00:48:59Z","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":"The family of temporal difference (TD) methods span a spectrum from computationally frugal linear methods like TD({\\lambda}) to data efficient least squares methods. Least square methods make the best use of available data directly computing the TD solution and thus do not require tuning a typically highly sensitive learning rate parameter, but require quadratic computation and storage. Recent algorithmic developments have yielded several sub-quadratic methods that use an approximation to the least squares TD solution, but incur bias. In this paper, we propose a new family of accelerated gradi","authors_text":"Adam White, Martha White, Yangchen Pan","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2016-11-28T20:33:15Z","title":"Accelerated Gradient Temporal Difference Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.09328","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:d3e6ecadf7610bbb36e4501a19641ed3bea5918a6d6bca95df3927f232b2a8af","target":"record","created_at":"2026-05-18T00:48:59Z","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":"b1d887ab80d44bd84b1e60df32af2284bd0d968389c0ce486018166e4b7b4ce6","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2016-11-28T20:33:15Z","title_canon_sha256":"c7c7904f14dd335bff33faa361c1464af033cc37625d79c501f1ddf7078d09dd"},"schema_version":"1.0","source":{"id":"1611.09328","kind":"arxiv","version":2}},"canonical_sha256":"5c6705f440ab7f763f4d54171d62bd6d259d664b7763d9709e7831b5f48ae218","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5c6705f440ab7f763f4d54171d62bd6d259d664b7763d9709e7831b5f48ae218","first_computed_at":"2026-05-18T00:48:59.620960Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:48:59.620960Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oqLiF4OOZRRQTK5Wa8nmzvgLRj6wpXewt/CH74Q3x9iixh3lM3RZQLwL/7TECUX/34tcYSVPnZkk5sgXF706AA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:48:59.621622Z","signed_message":"canonical_sha256_bytes"},"source_id":"1611.09328","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d3e6ecadf7610bbb36e4501a19641ed3bea5918a6d6bca95df3927f232b2a8af","sha256:630f9a94a1b280d93c0e42e70d4a8efcbdae10194a97cf364df399c18ce9363f"],"state_sha256":"ff95df6970a38a6abbc46a68889b19e74f7fef8995a7633bf9471508d158ebdc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/sreZb0rlyYpeGwWxLelcVI+dVU2qiVw0Q10YmBo9dQJ6L9VOOorMgyeIHV0OIpMhBfvPc4CRGb40hf+j89oDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T07:57:32.552824Z","bundle_sha256":"5d35a4ce62b4eb12d14a5c0657e126e79b4fd507481d85e88e8d26ec297f3b7c"}}