{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:OE6446OJN5Q76N2IU62YMKRZQO","short_pith_number":"pith:OE6446OJ","canonical_record":{"source":{"id":"2212.11360","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-21T20:53:44Z","cross_cats_sorted":[],"title_canon_sha256":"84577c3c1c7f863dae7e977abb4fc686d8c8ba3649ebe77655bc359c174f76e0","abstract_canon_sha256":"c74b49fea59377c6d1559e975d5b980d5c8f3a8556172e20b7baf7e71f535bc2"},"schema_version":"1.0"},"canonical_sha256":"713dce79c96f61ff3748a7b5862a3983ad07266e497ef28d51bcabe8e8145feb","source":{"kind":"arxiv","id":"2212.11360","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.11360","created_at":"2026-07-05T05:27:38Z"},{"alias_kind":"arxiv_version","alias_value":"2212.11360v1","created_at":"2026-07-05T05:27:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.11360","created_at":"2026-07-05T05:27:38Z"},{"alias_kind":"pith_short_12","alias_value":"OE6446OJN5Q7","created_at":"2026-07-05T05:27:38Z"},{"alias_kind":"pith_short_16","alias_value":"OE6446OJN5Q76N2I","created_at":"2026-07-05T05:27:38Z"},{"alias_kind":"pith_short_8","alias_value":"OE6446OJ","created_at":"2026-07-05T05:27:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:OE6446OJN5Q76N2IU62YMKRZQO","target":"record","payload":{"canonical_record":{"source":{"id":"2212.11360","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-21T20:53:44Z","cross_cats_sorted":[],"title_canon_sha256":"84577c3c1c7f863dae7e977abb4fc686d8c8ba3649ebe77655bc359c174f76e0","abstract_canon_sha256":"c74b49fea59377c6d1559e975d5b980d5c8f3a8556172e20b7baf7e71f535bc2"},"schema_version":"1.0"},"canonical_sha256":"713dce79c96f61ff3748a7b5862a3983ad07266e497ef28d51bcabe8e8145feb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:27:38.176247Z","signature_b64":"iGn4veTz6deww4gD6+ZfeQVzyFdbmu80fdRQLwYvveJi9hclmMPG+1sGtWNQPUphWQYV59A77/Pybf0AoluMBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"713dce79c96f61ff3748a7b5862a3983ad07266e497ef28d51bcabe8e8145feb","last_reissued_at":"2026-07-05T05:27:38.175758Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:27:38.175758Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2212.11360","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":"2026-07-05T05:27:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s0qZbc6Jv4GhQMYB2rApr2h8/LfyOZuNQ882Ey1BTnccJ4Jq6iH6N6UCiEfcJEX6SYqBmQQ4BFqUtJXvL8rfAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T03:36:39.679119Z"},"content_sha256":"45d5e707049cfcd4832ca2fa237d5162d3bfcbcb10ffe7c3bf702f7065a0a1e5","schema_version":"1.0","event_id":"sha256:45d5e707049cfcd4832ca2fa237d5162d3bfcbcb10ffe7c3bf702f7065a0a1e5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:OE6446OJN5Q76N2IU62YMKRZQO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Feature Acquisition using Monte Carlo Tree Search","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Diego Klabjan, Mark Shapiro, Sungsoo Lim","submitted_at":"2022-12-21T20:53:44Z","abstract_excerpt":"Feature acquisition algorithms address the problem of acquiring informative features while balancing the costs of acquisition to improve the learning performances of ML models. Previous approaches have focused on calculating the expected utility values of features to determine the acquisition sequences. Other approaches formulated the problem as a Markov Decision Process (MDP) and applied reinforcement learning based algorithms. In comparison to previous approaches, we focus on 1) formulating the feature acquisition problem as a MDP and applying Monte Carlo Tree Search, 2) calculating the inte"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.11360","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/2212.11360/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":"2026-07-05T05:27:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tmJJg7kkfrhqwK+UnmYwZWrPLvV+wCcNr3c4Ph+10FUHHMb2OgT3FZ+mfgfDHVBGSpMEmzOXU0JH6RzdF8ONDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T03:36:39.679581Z"},"content_sha256":"c32c9e4009809df3c682c5b76fd1b5e98733acaac9b8b5f6853f0e6baf481c8a","schema_version":"1.0","event_id":"sha256:c32c9e4009809df3c682c5b76fd1b5e98733acaac9b8b5f6853f0e6baf481c8a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OE6446OJN5Q76N2IU62YMKRZQO/bundle.json","state_url":"https://pith.science/pith/OE6446OJN5Q76N2IU62YMKRZQO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OE6446OJN5Q76N2IU62YMKRZQO/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":"2026-07-08T03:36:39Z","links":{"resolver":"https://pith.science/pith/OE6446OJN5Q76N2IU62YMKRZQO","bundle":"https://pith.science/pith/OE6446OJN5Q76N2IU62YMKRZQO/bundle.json","state":"https://pith.science/pith/OE6446OJN5Q76N2IU62YMKRZQO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OE6446OJN5Q76N2IU62YMKRZQO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:OE6446OJN5Q76N2IU62YMKRZQO","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":"c74b49fea59377c6d1559e975d5b980d5c8f3a8556172e20b7baf7e71f535bc2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-21T20:53:44Z","title_canon_sha256":"84577c3c1c7f863dae7e977abb4fc686d8c8ba3649ebe77655bc359c174f76e0"},"schema_version":"1.0","source":{"id":"2212.11360","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.11360","created_at":"2026-07-05T05:27:38Z"},{"alias_kind":"arxiv_version","alias_value":"2212.11360v1","created_at":"2026-07-05T05:27:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.11360","created_at":"2026-07-05T05:27:38Z"},{"alias_kind":"pith_short_12","alias_value":"OE6446OJN5Q7","created_at":"2026-07-05T05:27:38Z"},{"alias_kind":"pith_short_16","alias_value":"OE6446OJN5Q76N2I","created_at":"2026-07-05T05:27:38Z"},{"alias_kind":"pith_short_8","alias_value":"OE6446OJ","created_at":"2026-07-05T05:27:38Z"}],"graph_snapshots":[{"event_id":"sha256:c32c9e4009809df3c682c5b76fd1b5e98733acaac9b8b5f6853f0e6baf481c8a","target":"graph","created_at":"2026-07-05T05:27:38Z","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/2212.11360/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Feature acquisition algorithms address the problem of acquiring informative features while balancing the costs of acquisition to improve the learning performances of ML models. Previous approaches have focused on calculating the expected utility values of features to determine the acquisition sequences. Other approaches formulated the problem as a Markov Decision Process (MDP) and applied reinforcement learning based algorithms. In comparison to previous approaches, we focus on 1) formulating the feature acquisition problem as a MDP and applying Monte Carlo Tree Search, 2) calculating the inte","authors_text":"Diego Klabjan, Mark Shapiro, Sungsoo Lim","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-21T20:53:44Z","title":"Feature Acquisition using Monte Carlo Tree Search"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.11360","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":"sha256:45d5e707049cfcd4832ca2fa237d5162d3bfcbcb10ffe7c3bf702f7065a0a1e5","target":"record","created_at":"2026-07-05T05:27:38Z","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":"c74b49fea59377c6d1559e975d5b980d5c8f3a8556172e20b7baf7e71f535bc2","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-21T20:53:44Z","title_canon_sha256":"84577c3c1c7f863dae7e977abb4fc686d8c8ba3649ebe77655bc359c174f76e0"},"schema_version":"1.0","source":{"id":"2212.11360","kind":"arxiv","version":1}},"canonical_sha256":"713dce79c96f61ff3748a7b5862a3983ad07266e497ef28d51bcabe8e8145feb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"713dce79c96f61ff3748a7b5862a3983ad07266e497ef28d51bcabe8e8145feb","first_computed_at":"2026-07-05T05:27:38.175758Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:27:38.175758Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"iGn4veTz6deww4gD6+ZfeQVzyFdbmu80fdRQLwYvveJi9hclmMPG+1sGtWNQPUphWQYV59A77/Pybf0AoluMBw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:27:38.176247Z","signed_message":"canonical_sha256_bytes"},"source_id":"2212.11360","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:45d5e707049cfcd4832ca2fa237d5162d3bfcbcb10ffe7c3bf702f7065a0a1e5","sha256:c32c9e4009809df3c682c5b76fd1b5e98733acaac9b8b5f6853f0e6baf481c8a"],"state_sha256":"e351239706a26fef0a96cf906605631afc03b60b6ca3798cee9acc1d8701714c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"b8dU5RBD+Y0raOIxF74AZDV/7qG37gZSj6okMbcxpfxakUkLG/rfyDJIL7DKAL5SUVLiLwtk6q0F0nJF/CaLAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-08T03:36:39.682985Z","bundle_sha256":"cf480785a112bbeaaf10612e3c0e14a56d9102debe48785396f6380234ad95d5"}}