{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:7DQJWOX2JYK5VZKT3C7J4PLJJD","short_pith_number":"pith:7DQJWOX2","schema_version":"1.0","canonical_sha256":"f8e09b3afa4e15dae553d8be9e3d6948ea53fc6d3fee3d9f63d37f9b3dd631a8","source":{"kind":"arxiv","id":"1810.00093","version":2},"attestation_state":"computed","paper":{"title":"Barrier Certificates for Assured Machine Teaching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Bo Wu, Mohamadreza Ahmadi, Ufuk Topcu, Yisong Yue, Yuxin Chen","submitted_at":"2018-09-28T21:45:12Z","abstract_excerpt":"Machine teaching can be viewed as optimal control for learning. Given a learner's model, machine teaching aims to determine the optimal training data to steer the learner towards a target hypothesis. In this paper, we are interested in providing assurances for machine teaching algorithms using control theory. In particular, we study a well-established learner's model in the machine teaching literature that is captured by the local preference over a version space. We interpret the problem of teaching a preference-based learner as solving a partially observable Markov decision process (POMDP). W"},"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":"1810.00093","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2018-09-28T21:45:12Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"d91c45f99d3d5718bae7af3c94598ed57aa3af01cd9ab534ea808538afa266db","abstract_canon_sha256":"518ab926fd1e4d69c50f473d79b237cab311bcab336d7368c17a4975b8ca7b16"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-04T20:13:23.841591Z","signature_b64":"/k9rmo5rG4FYRoOgDvyHxmIj/3NEECJFHa/dP7XEZRRqlssu+YKDcRCkyg0nxs3k0O2KYzBhKRqthP+iyqlCCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f8e09b3afa4e15dae553d8be9e3d6948ea53fc6d3fee3d9f63d37f9b3dd631a8","last_reissued_at":"2026-06-04T20:13:23.841161Z","signature_status":"signed_v1","first_computed_at":"2026-06-04T20:13:23.841161Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Barrier Certificates for Assured Machine Teaching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Bo Wu, Mohamadreza Ahmadi, Ufuk Topcu, Yisong Yue, Yuxin Chen","submitted_at":"2018-09-28T21:45:12Z","abstract_excerpt":"Machine teaching can be viewed as optimal control for learning. Given a learner's model, machine teaching aims to determine the optimal training data to steer the learner towards a target hypothesis. In this paper, we are interested in providing assurances for machine teaching algorithms using control theory. In particular, we study a well-established learner's model in the machine teaching literature that is captured by the local preference over a version space. We interpret the problem of teaching a preference-based learner as solving a partially observable Markov decision process (POMDP). W"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.00093","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1810.00093/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":"1810.00093","created_at":"2026-06-04T20:13:23.841219+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.00093v2","created_at":"2026-06-04T20:13:23.841219+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.00093","created_at":"2026-06-04T20:13:23.841219+00:00"},{"alias_kind":"pith_short_12","alias_value":"7DQJWOX2JYK5","created_at":"2026-06-04T20:13:23.841219+00:00"},{"alias_kind":"pith_short_16","alias_value":"7DQJWOX2JYK5VZKT","created_at":"2026-06-04T20:13:23.841219+00:00"},{"alias_kind":"pith_short_8","alias_value":"7DQJWOX2","created_at":"2026-06-04T20:13:23.841219+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/7DQJWOX2JYK5VZKT3C7J4PLJJD","json":"https://pith.science/pith/7DQJWOX2JYK5VZKT3C7J4PLJJD.json","graph_json":"https://pith.science/api/pith-number/7DQJWOX2JYK5VZKT3C7J4PLJJD/graph.json","events_json":"https://pith.science/api/pith-number/7DQJWOX2JYK5VZKT3C7J4PLJJD/events.json","paper":"https://pith.science/paper/7DQJWOX2"},"agent_actions":{"view_html":"https://pith.science/pith/7DQJWOX2JYK5VZKT3C7J4PLJJD","download_json":"https://pith.science/pith/7DQJWOX2JYK5VZKT3C7J4PLJJD.json","view_paper":"https://pith.science/paper/7DQJWOX2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.00093&json=true","fetch_graph":"https://pith.science/api/pith-number/7DQJWOX2JYK5VZKT3C7J4PLJJD/graph.json","fetch_events":"https://pith.science/api/pith-number/7DQJWOX2JYK5VZKT3C7J4PLJJD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7DQJWOX2JYK5VZKT3C7J4PLJJD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7DQJWOX2JYK5VZKT3C7J4PLJJD/action/storage_attestation","attest_author":"https://pith.science/pith/7DQJWOX2JYK5VZKT3C7J4PLJJD/action/author_attestation","sign_citation":"https://pith.science/pith/7DQJWOX2JYK5VZKT3C7J4PLJJD/action/citation_signature","submit_replication":"https://pith.science/pith/7DQJWOX2JYK5VZKT3C7J4PLJJD/action/replication_record"}},"created_at":"2026-06-04T20:13:23.841219+00:00","updated_at":"2026-06-04T20:13:23.841219+00:00"}