{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:ECPKXXACU25KLAX66LWVR2KZKP","short_pith_number":"pith:ECPKXXAC","schema_version":"1.0","canonical_sha256":"209eabdc02a6baa582fef2ed58e95953c4b6339af2c177c5d6f20bd5dfc250a2","source":{"kind":"arxiv","id":"2210.17467","version":2},"attestation_state":"computed","paper":{"title":"Iterative Teaching by Data Hallucination","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Adrian Weller, Bernhard Sch\\\"olkopf, Tim Z. Xiao, Umang Bhatt, Weiyang Liu, Yucen Luo, Zeju Qiu, Zhen Liu","submitted_at":"2022-10-31T16:48:47Z","abstract_excerpt":"We consider the problem of iterative machine teaching, where a teacher sequentially provides examples based on the status of a learner under a discrete input space (i.e., a pool of finite samples), which greatly limits the teacher's capability. To address this issue, we study iterative teaching under a continuous input space where the input example (i.e., image) can be either generated by solving an optimization problem or drawn directly from a continuous distribution. Specifically, we propose data hallucination teaching (DHT) where the teacher can generate input data intelligently based on la"},"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":"2210.17467","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-10-31T16:48:47Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"e340a286b8f69fb44a39285a8a6600fede1ae9febce0485824f4281c99ee3491","abstract_canon_sha256":"a0731c8b18126c1c6d48be46b150b152daf96581a263398cd1681b546aae50b2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:00:33.242971Z","signature_b64":"yfwq+6tC9BLwwfWWejcyN2Q9tqLW4AtvoaJl5l7oOGBXuRJ99EP0C+FhcQBsrs1fVudKYk5zMDc4iWl1B7OICA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"209eabdc02a6baa582fef2ed58e95953c4b6339af2c177c5d6f20bd5dfc250a2","last_reissued_at":"2026-07-05T06:00:33.242426Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:00:33.242426Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Iterative Teaching by Data Hallucination","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Adrian Weller, Bernhard Sch\\\"olkopf, Tim Z. Xiao, Umang Bhatt, Weiyang Liu, Yucen Luo, Zeju Qiu, Zhen Liu","submitted_at":"2022-10-31T16:48:47Z","abstract_excerpt":"We consider the problem of iterative machine teaching, where a teacher sequentially provides examples based on the status of a learner under a discrete input space (i.e., a pool of finite samples), which greatly limits the teacher's capability. To address this issue, we study iterative teaching under a continuous input space where the input example (i.e., image) can be either generated by solving an optimization problem or drawn directly from a continuous distribution. Specifically, we propose data hallucination teaching (DHT) where the teacher can generate input data intelligently based on la"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.17467","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/2210.17467/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":"2210.17467","created_at":"2026-07-05T06:00:33.242528+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.17467v2","created_at":"2026-07-05T06:00:33.242528+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.17467","created_at":"2026-07-05T06:00:33.242528+00:00"},{"alias_kind":"pith_short_12","alias_value":"ECPKXXACU25K","created_at":"2026-07-05T06:00:33.242528+00:00"},{"alias_kind":"pith_short_16","alias_value":"ECPKXXACU25KLAX6","created_at":"2026-07-05T06:00:33.242528+00:00"},{"alias_kind":"pith_short_8","alias_value":"ECPKXXAC","created_at":"2026-07-05T06:00:33.242528+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/ECPKXXACU25KLAX66LWVR2KZKP","json":"https://pith.science/pith/ECPKXXACU25KLAX66LWVR2KZKP.json","graph_json":"https://pith.science/api/pith-number/ECPKXXACU25KLAX66LWVR2KZKP/graph.json","events_json":"https://pith.science/api/pith-number/ECPKXXACU25KLAX66LWVR2KZKP/events.json","paper":"https://pith.science/paper/ECPKXXAC"},"agent_actions":{"view_html":"https://pith.science/pith/ECPKXXACU25KLAX66LWVR2KZKP","download_json":"https://pith.science/pith/ECPKXXACU25KLAX66LWVR2KZKP.json","view_paper":"https://pith.science/paper/ECPKXXAC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.17467&json=true","fetch_graph":"https://pith.science/api/pith-number/ECPKXXACU25KLAX66LWVR2KZKP/graph.json","fetch_events":"https://pith.science/api/pith-number/ECPKXXACU25KLAX66LWVR2KZKP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ECPKXXACU25KLAX66LWVR2KZKP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ECPKXXACU25KLAX66LWVR2KZKP/action/storage_attestation","attest_author":"https://pith.science/pith/ECPKXXACU25KLAX66LWVR2KZKP/action/author_attestation","sign_citation":"https://pith.science/pith/ECPKXXACU25KLAX66LWVR2KZKP/action/citation_signature","submit_replication":"https://pith.science/pith/ECPKXXACU25KLAX66LWVR2KZKP/action/replication_record"}},"created_at":"2026-07-05T06:00:33.242528+00:00","updated_at":"2026-07-05T06:00:33.242528+00:00"}