{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:SZ4BDMMNBRLQ2L32SHLGK2ZBGB","short_pith_number":"pith:SZ4BDMMN","canonical_record":{"source":{"id":"2402.15053","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-02-23T02:14:44Z","cross_cats_sorted":["cs.LG","stat.ME"],"title_canon_sha256":"ed3274e5fdca25f2014845b2bdfbb6246a47462b8cfd44128ba95524ed0cfc65","abstract_canon_sha256":"e5028560288313e8fa00cf3f53ebb7ab3f3ae145414c9085c9c6b99f791cfa92"},"schema_version":"1.0"},"canonical_sha256":"967811b18d0c570d2f7a91d6656b213044e0a43bad9b1cdff04c7f29a5260ab6","source":{"kind":"arxiv","id":"2402.15053","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.15053","created_at":"2026-07-05T07:48:21Z"},{"alias_kind":"arxiv_version","alias_value":"2402.15053v1","created_at":"2026-07-05T07:48:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.15053","created_at":"2026-07-05T07:48:21Z"},{"alias_kind":"pith_short_12","alias_value":"SZ4BDMMNBRLQ","created_at":"2026-07-05T07:48:21Z"},{"alias_kind":"pith_short_16","alias_value":"SZ4BDMMNBRLQ2L32","created_at":"2026-07-05T07:48:21Z"},{"alias_kind":"pith_short_8","alias_value":"SZ4BDMMN","created_at":"2026-07-05T07:48:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:SZ4BDMMNBRLQ2L32SHLGK2ZBGB","target":"record","payload":{"canonical_record":{"source":{"id":"2402.15053","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-02-23T02:14:44Z","cross_cats_sorted":["cs.LG","stat.ME"],"title_canon_sha256":"ed3274e5fdca25f2014845b2bdfbb6246a47462b8cfd44128ba95524ed0cfc65","abstract_canon_sha256":"e5028560288313e8fa00cf3f53ebb7ab3f3ae145414c9085c9c6b99f791cfa92"},"schema_version":"1.0"},"canonical_sha256":"967811b18d0c570d2f7a91d6656b213044e0a43bad9b1cdff04c7f29a5260ab6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:48:21.311831Z","signature_b64":"AnF8f06TX4cpDTa8eaxCvQPnMw8xjZMGFut6GDKWDYKIpwo4ZhYtZ4/yVuCd6mUK006Mb26jvOkoTSw1TwlUDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"967811b18d0c570d2f7a91d6656b213044e0a43bad9b1cdff04c7f29a5260ab6","last_reissued_at":"2026-07-05T07:48:21.311455Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:48:21.311455Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.15053","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:48:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pAzkQqhmWo4xIkUTkwDcr8u7vKV7LTyOZLJM7dL4BsIF6hB9YA0qZi5VyIUuYTPy7HisLzQdTVZm7vyxWGefAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T15:10:23.288411Z"},"content_sha256":"235e7f28eeefc9a921b77e927707dd5620c071b1f41be8a26a30a2df8127d91f","schema_version":"1.0","event_id":"sha256:235e7f28eeefc9a921b77e927707dd5620c071b1f41be8a26a30a2df8127d91f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:SZ4BDMMNBRLQ2L32SHLGK2ZBGB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Nonlinear Bayesian optimal experimental design using logarithmic Sobolev inequalities","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ME"],"primary_cat":"stat.ML","authors_text":"Ayoub Belhadji, Fengyi Li, Youssef Marzouk","submitted_at":"2024-02-23T02:14:44Z","abstract_excerpt":"We study the problem of selecting $k$ experiments from a larger candidate pool, where the goal is to maximize mutual information (MI) between the selected subset and the underlying parameters. Finding the exact solution is to this combinatorial optimization problem is computationally costly, not only due to the complexity of the combinatorial search but also the difficulty of evaluating MI in nonlinear/non-Gaussian settings. We propose greedy approaches based on new computationally inexpensive lower bounds for MI, constructed via log-Sobolev inequalities. We demonstrate that our method outperf"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.15053","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/2402.15053/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":"2026-07-05T07:48:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4E3xEOl8rHvkxlCBDWvG+zlP+95N+eNc49ECsQW0aeRqZ7glamCVPJT53Ubkra26ADcOPuDrhtvfdKVE8ARfAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T15:10:23.288883Z"},"content_sha256":"4b810baef34fb86ebf1f1c4f0d4f22468d9fb0262611da227e060b5c219e8db0","schema_version":"1.0","event_id":"sha256:4b810baef34fb86ebf1f1c4f0d4f22468d9fb0262611da227e060b5c219e8db0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SZ4BDMMNBRLQ2L32SHLGK2ZBGB/bundle.json","state_url":"https://pith.science/pith/SZ4BDMMNBRLQ2L32SHLGK2ZBGB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SZ4BDMMNBRLQ2L32SHLGK2ZBGB/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":"2026-08-15T15:10:23Z","links":{"resolver":"https://pith.science/pith/SZ4BDMMNBRLQ2L32SHLGK2ZBGB","bundle":"https://pith.science/pith/SZ4BDMMNBRLQ2L32SHLGK2ZBGB/bundle.json","state":"https://pith.science/pith/SZ4BDMMNBRLQ2L32SHLGK2ZBGB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SZ4BDMMNBRLQ2L32SHLGK2ZBGB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:SZ4BDMMNBRLQ2L32SHLGK2ZBGB","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":"e5028560288313e8fa00cf3f53ebb7ab3f3ae145414c9085c9c6b99f791cfa92","cross_cats_sorted":["cs.LG","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-02-23T02:14:44Z","title_canon_sha256":"ed3274e5fdca25f2014845b2bdfbb6246a47462b8cfd44128ba95524ed0cfc65"},"schema_version":"1.0","source":{"id":"2402.15053","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.15053","created_at":"2026-07-05T07:48:21Z"},{"alias_kind":"arxiv_version","alias_value":"2402.15053v1","created_at":"2026-07-05T07:48:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.15053","created_at":"2026-07-05T07:48:21Z"},{"alias_kind":"pith_short_12","alias_value":"SZ4BDMMNBRLQ","created_at":"2026-07-05T07:48:21Z"},{"alias_kind":"pith_short_16","alias_value":"SZ4BDMMNBRLQ2L32","created_at":"2026-07-05T07:48:21Z"},{"alias_kind":"pith_short_8","alias_value":"SZ4BDMMN","created_at":"2026-07-05T07:48:21Z"}],"graph_snapshots":[{"event_id":"sha256:4b810baef34fb86ebf1f1c4f0d4f22468d9fb0262611da227e060b5c219e8db0","target":"graph","created_at":"2026-07-05T07:48:21Z","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/2402.15053/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We study the problem of selecting $k$ experiments from a larger candidate pool, where the goal is to maximize mutual information (MI) between the selected subset and the underlying parameters. Finding the exact solution is to this combinatorial optimization problem is computationally costly, not only due to the complexity of the combinatorial search but also the difficulty of evaluating MI in nonlinear/non-Gaussian settings. We propose greedy approaches based on new computationally inexpensive lower bounds for MI, constructed via log-Sobolev inequalities. We demonstrate that our method outperf","authors_text":"Ayoub Belhadji, Fengyi Li, Youssef Marzouk","cross_cats":["cs.LG","stat.ME"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-02-23T02:14:44Z","title":"Nonlinear Bayesian optimal experimental design using logarithmic Sobolev inequalities"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.15053","kind":"arxiv","version":1},"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":"sha256:235e7f28eeefc9a921b77e927707dd5620c071b1f41be8a26a30a2df8127d91f","target":"record","created_at":"2026-07-05T07:48:21Z","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":"e5028560288313e8fa00cf3f53ebb7ab3f3ae145414c9085c9c6b99f791cfa92","cross_cats_sorted":["cs.LG","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-02-23T02:14:44Z","title_canon_sha256":"ed3274e5fdca25f2014845b2bdfbb6246a47462b8cfd44128ba95524ed0cfc65"},"schema_version":"1.0","source":{"id":"2402.15053","kind":"arxiv","version":1}},"canonical_sha256":"967811b18d0c570d2f7a91d6656b213044e0a43bad9b1cdff04c7f29a5260ab6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"967811b18d0c570d2f7a91d6656b213044e0a43bad9b1cdff04c7f29a5260ab6","first_computed_at":"2026-07-05T07:48:21.311455Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:48:21.311455Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AnF8f06TX4cpDTa8eaxCvQPnMw8xjZMGFut6GDKWDYKIpwo4ZhYtZ4/yVuCd6mUK006Mb26jvOkoTSw1TwlUDA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:48:21.311831Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.15053","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:235e7f28eeefc9a921b77e927707dd5620c071b1f41be8a26a30a2df8127d91f","sha256:4b810baef34fb86ebf1f1c4f0d4f22468d9fb0262611da227e060b5c219e8db0"],"state_sha256":"0a709ce5b74ab98e5d23f68fe63a9b5f7d8a8359684207b799986cf36d3f2249"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xNlntAK2swnFe8WSWtAojCM1VsDnsJvYe8Bw89X4Jcm67b6KUbjSsfnqlNhcVdCTiyLAGnxgFWWrK4QVoWW6Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T15:10:23.292423Z","bundle_sha256":"34c8bc987fb3a1561d903b6e1eb78733b92c4a9684ec94dd73301ecb87847db4"}}