{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:6CX3TIEBFP5XNBF2TKLCXZPCCC","short_pith_number":"pith:6CX3TIEB","schema_version":"1.0","canonical_sha256":"f0afb9a0812bfb7684ba9a962be5e210877064af54b5068ec45e1a29e372dff0","source":{"kind":"arxiv","id":"2606.05935","version":1},"attestation_state":"computed","paper":{"title":"Hessian-informed, Coordinate Friendly Hamiltonian Monte Carlo in Linear Time","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"stat.CO","authors_text":"Alexandre Bouchard-C\\^ot\\'e, Nikola Surjanovic, Son Luu, Trevor Campbell, Zuheng Xu","submitted_at":"2026-06-04T09:37:55Z","abstract_excerpt":"Riemannian Hamiltonian Monte Carlo (RHMC) is a promising MCMC methodology thanks to its ability to accommodate position-dependent preconditioning and multi-step proposals. While RHMC performs well in low dimensions, it becomes infeasible in high dimensions due to its $O(d^3)$ cost per fixed-point iteration, where $d$ is the dimension of the target density. Even when the position-dependent preconditioner is based on the diagonal of the Hessian, the cost is still $O(d^2)$ per fixed-point iteration. In this paper, we propose a computational method to reduce the computational complexity of RHMC fi"},"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":"2606.05935","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2026-06-04T09:37:55Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"5aeb014ee2b7d1f0138317bab737dfb8fd56bca3e09fdb429f97508c192d2e88","abstract_canon_sha256":"7801c1151daed9395d8baf7eefd97bfa9a72951f7eaffc497bcf718c515ccf57"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-05T01:15:28.191614Z","signature_b64":"bTzgy938jAcm4lzGQw8tDgUv6HWtOzbEoObGNK/iYWP6mWCmU5iXRaL4pTqUWKX18YeVPgMqoSq/BfEEROy3Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f0afb9a0812bfb7684ba9a962be5e210877064af54b5068ec45e1a29e372dff0","last_reissued_at":"2026-06-05T01:15:28.190620Z","signature_status":"signed_v1","first_computed_at":"2026-06-05T01:15:28.190620Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hessian-informed, Coordinate Friendly Hamiltonian Monte Carlo in Linear Time","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"stat.CO","authors_text":"Alexandre Bouchard-C\\^ot\\'e, Nikola Surjanovic, Son Luu, Trevor Campbell, Zuheng Xu","submitted_at":"2026-06-04T09:37:55Z","abstract_excerpt":"Riemannian Hamiltonian Monte Carlo (RHMC) is a promising MCMC methodology thanks to its ability to accommodate position-dependent preconditioning and multi-step proposals. While RHMC performs well in low dimensions, it becomes infeasible in high dimensions due to its $O(d^3)$ cost per fixed-point iteration, where $d$ is the dimension of the target density. Even when the position-dependent preconditioner is based on the diagonal of the Hessian, the cost is still $O(d^2)$ per fixed-point iteration. In this paper, we propose a computational method to reduce the computational complexity of RHMC fi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.05935","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.05935/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.05935","created_at":"2026-06-05T01:15:28.190690+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.05935v1","created_at":"2026-06-05T01:15:28.190690+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.05935","created_at":"2026-06-05T01:15:28.190690+00:00"},{"alias_kind":"pith_short_12","alias_value":"6CX3TIEBFP5X","created_at":"2026-06-05T01:15:28.190690+00:00"},{"alias_kind":"pith_short_16","alias_value":"6CX3TIEBFP5XNBF2","created_at":"2026-06-05T01:15:28.190690+00:00"},{"alias_kind":"pith_short_8","alias_value":"6CX3TIEB","created_at":"2026-06-05T01:15:28.190690+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/6CX3TIEBFP5XNBF2TKLCXZPCCC","json":"https://pith.science/pith/6CX3TIEBFP5XNBF2TKLCXZPCCC.json","graph_json":"https://pith.science/api/pith-number/6CX3TIEBFP5XNBF2TKLCXZPCCC/graph.json","events_json":"https://pith.science/api/pith-number/6CX3TIEBFP5XNBF2TKLCXZPCCC/events.json","paper":"https://pith.science/paper/6CX3TIEB"},"agent_actions":{"view_html":"https://pith.science/pith/6CX3TIEBFP5XNBF2TKLCXZPCCC","download_json":"https://pith.science/pith/6CX3TIEBFP5XNBF2TKLCXZPCCC.json","view_paper":"https://pith.science/paper/6CX3TIEB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.05935&json=true","fetch_graph":"https://pith.science/api/pith-number/6CX3TIEBFP5XNBF2TKLCXZPCCC/graph.json","fetch_events":"https://pith.science/api/pith-number/6CX3TIEBFP5XNBF2TKLCXZPCCC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6CX3TIEBFP5XNBF2TKLCXZPCCC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6CX3TIEBFP5XNBF2TKLCXZPCCC/action/storage_attestation","attest_author":"https://pith.science/pith/6CX3TIEBFP5XNBF2TKLCXZPCCC/action/author_attestation","sign_citation":"https://pith.science/pith/6CX3TIEBFP5XNBF2TKLCXZPCCC/action/citation_signature","submit_replication":"https://pith.science/pith/6CX3TIEBFP5XNBF2TKLCXZPCCC/action/replication_record"}},"created_at":"2026-06-05T01:15:28.190690+00:00","updated_at":"2026-06-05T01:15:28.190690+00:00"}