{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:2LYSCAHPKDMZ3O6SHIDLS7ZIN5","short_pith_number":"pith:2LYSCAHP","schema_version":"1.0","canonical_sha256":"d2f12100ef50d99dbbd23a06b97f286f617f45620e93b86283591674f4658d99","source":{"kind":"arxiv","id":"2402.07868","version":4},"attestation_state":"computed","paper":{"title":"Nesting Particle Filters for Experimental Design in Dynamical Systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","stat.ME"],"primary_cat":"stat.ML","authors_text":"Adrien Corenflos, Hany Abdulsamad, Sahel Iqbal, Simo S\\\"arkk\\\"a","submitted_at":"2024-02-12T18:29:17Z","abstract_excerpt":"In this paper, we propose a novel approach to Bayesian experimental design for non-exchangeable data that formulates it as risk-sensitive policy optimization. We develop the Inside-Out SMC$^2$ algorithm, a nested sequential Monte Carlo technique to infer optimal designs, and embed it into a particle Markov chain Monte Carlo framework to perform gradient-based policy amortization. Our approach is distinct from other amortized experimental design techniques, as it does not rely on contrastive estimators. Numerical validation on a set of dynamical systems showcases the efficacy of our method in c"},"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":"2402.07868","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-02-12T18:29:17Z","cross_cats_sorted":["cs.LG","stat.ME"],"title_canon_sha256":"f69c29f3d46ea035f2ab78714edf1ce4e3818bf8a2480f9f91831885603a531b","abstract_canon_sha256":"6e1a9b19088d2404ea17d4b19dda89729bba2c43de550e434e675013fdd6ff4c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:24:41.069167Z","signature_b64":"GuYta0xCXHuFz91Ld864GwLYcHh6gVDsRuxyhOcL6WsHYFbDvPVdLYk7nW1eHf0eEEA0GpykRsKyqPyXSDnfDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d2f12100ef50d99dbbd23a06b97f286f617f45620e93b86283591674f4658d99","last_reissued_at":"2026-07-05T08:24:41.068720Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:24:41.068720Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Nesting Particle Filters for Experimental Design in Dynamical Systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","stat.ME"],"primary_cat":"stat.ML","authors_text":"Adrien Corenflos, Hany Abdulsamad, Sahel Iqbal, Simo S\\\"arkk\\\"a","submitted_at":"2024-02-12T18:29:17Z","abstract_excerpt":"In this paper, we propose a novel approach to Bayesian experimental design for non-exchangeable data that formulates it as risk-sensitive policy optimization. We develop the Inside-Out SMC$^2$ algorithm, a nested sequential Monte Carlo technique to infer optimal designs, and embed it into a particle Markov chain Monte Carlo framework to perform gradient-based policy amortization. Our approach is distinct from other amortized experimental design techniques, as it does not rely on contrastive estimators. Numerical validation on a set of dynamical systems showcases the efficacy of our method in c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.07868","kind":"arxiv","version":4},"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/2402.07868/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":"2402.07868","created_at":"2026-07-05T08:24:41.068782+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.07868v4","created_at":"2026-07-05T08:24:41.068782+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.07868","created_at":"2026-07-05T08:24:41.068782+00:00"},{"alias_kind":"pith_short_12","alias_value":"2LYSCAHPKDMZ","created_at":"2026-07-05T08:24:41.068782+00:00"},{"alias_kind":"pith_short_16","alias_value":"2LYSCAHPKDMZ3O6S","created_at":"2026-07-05T08:24:41.068782+00:00"},{"alias_kind":"pith_short_8","alias_value":"2LYSCAHP","created_at":"2026-07-05T08:24:41.068782+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.23515","citing_title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","ref_index":46,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2LYSCAHPKDMZ3O6SHIDLS7ZIN5","json":"https://pith.science/pith/2LYSCAHPKDMZ3O6SHIDLS7ZIN5.json","graph_json":"https://pith.science/api/pith-number/2LYSCAHPKDMZ3O6SHIDLS7ZIN5/graph.json","events_json":"https://pith.science/api/pith-number/2LYSCAHPKDMZ3O6SHIDLS7ZIN5/events.json","paper":"https://pith.science/paper/2LYSCAHP"},"agent_actions":{"view_html":"https://pith.science/pith/2LYSCAHPKDMZ3O6SHIDLS7ZIN5","download_json":"https://pith.science/pith/2LYSCAHPKDMZ3O6SHIDLS7ZIN5.json","view_paper":"https://pith.science/paper/2LYSCAHP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.07868&json=true","fetch_graph":"https://pith.science/api/pith-number/2LYSCAHPKDMZ3O6SHIDLS7ZIN5/graph.json","fetch_events":"https://pith.science/api/pith-number/2LYSCAHPKDMZ3O6SHIDLS7ZIN5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2LYSCAHPKDMZ3O6SHIDLS7ZIN5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2LYSCAHPKDMZ3O6SHIDLS7ZIN5/action/storage_attestation","attest_author":"https://pith.science/pith/2LYSCAHPKDMZ3O6SHIDLS7ZIN5/action/author_attestation","sign_citation":"https://pith.science/pith/2LYSCAHPKDMZ3O6SHIDLS7ZIN5/action/citation_signature","submit_replication":"https://pith.science/pith/2LYSCAHPKDMZ3O6SHIDLS7ZIN5/action/replication_record"}},"created_at":"2026-07-05T08:24:41.068782+00:00","updated_at":"2026-07-05T08:24:41.068782+00:00"}