{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:HCMD5UFTK7MRHXF6FI2LTYUWL7","short_pith_number":"pith:HCMD5UFT","schema_version":"1.0","canonical_sha256":"38983ed0b357d913dcbe2a34b9e2965fe2a00f82344a40228bdc84f8fbbbe2e0","source":{"kind":"arxiv","id":"1808.05334","version":1},"attestation_state":"computed","paper":{"title":"Active Distribution Learning from Indirect Samples","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT","stat.ML"],"primary_cat":"cs.LG","authors_text":"Gauri Joshi, Osman Ya\\u{g}an, Samarth Gupta","submitted_at":"2018-08-16T03:25:09Z","abstract_excerpt":"This paper studies the problem of {\\em learning} the probability distribution $P_X$ of a discrete random variable $X$ using indirect and sequential samples. At each time step, we choose one of the possible $K$ functions, $g_1, \\ldots, g_K$ and observe the corresponding sample $g_i(X)$. The goal is to estimate the probability distribution of $X$ by using a minimum number of such sequential samples. This problem has several real-world applications including inference under non-precise information and privacy-preserving statistical estimation. We establish necessary and sufficient conditions on t"},"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":"1808.05334","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-08-16T03:25:09Z","cross_cats_sorted":["cs.IT","math.IT","stat.ML"],"title_canon_sha256":"7d7d2ad0503288ce40fbbf5dd2a1617bc0a89f24d4e13e6f95e7c9e8617a2537","abstract_canon_sha256":"de648fd50125fb4e36d77ab003827ed0d987185a6b21dd6e7a1e114dd09cd4cc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:07:57.508686Z","signature_b64":"JVp6may7JbqZzuz+VrCzO9IxaG62TJIOclFk3sGO2md/WAZJAOap5cYbWKFD8zhpdng9dONE8RjiuafN7U6bCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"38983ed0b357d913dcbe2a34b9e2965fe2a00f82344a40228bdc84f8fbbbe2e0","last_reissued_at":"2026-05-18T00:07:57.507984Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:07:57.507984Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Active Distribution Learning from Indirect Samples","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT","stat.ML"],"primary_cat":"cs.LG","authors_text":"Gauri Joshi, Osman Ya\\u{g}an, Samarth Gupta","submitted_at":"2018-08-16T03:25:09Z","abstract_excerpt":"This paper studies the problem of {\\em learning} the probability distribution $P_X$ of a discrete random variable $X$ using indirect and sequential samples. At each time step, we choose one of the possible $K$ functions, $g_1, \\ldots, g_K$ and observe the corresponding sample $g_i(X)$. The goal is to estimate the probability distribution of $X$ by using a minimum number of such sequential samples. This problem has several real-world applications including inference under non-precise information and privacy-preserving statistical estimation. We establish necessary and sufficient conditions on t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.05334","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":""},"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":"1808.05334","created_at":"2026-05-18T00:07:57.508103+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.05334v1","created_at":"2026-05-18T00:07:57.508103+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.05334","created_at":"2026-05-18T00:07:57.508103+00:00"},{"alias_kind":"pith_short_12","alias_value":"HCMD5UFTK7MR","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_16","alias_value":"HCMD5UFTK7MRHXF6","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_8","alias_value":"HCMD5UFT","created_at":"2026-05-18T12:32:28.185984+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/HCMD5UFTK7MRHXF6FI2LTYUWL7","json":"https://pith.science/pith/HCMD5UFTK7MRHXF6FI2LTYUWL7.json","graph_json":"https://pith.science/api/pith-number/HCMD5UFTK7MRHXF6FI2LTYUWL7/graph.json","events_json":"https://pith.science/api/pith-number/HCMD5UFTK7MRHXF6FI2LTYUWL7/events.json","paper":"https://pith.science/paper/HCMD5UFT"},"agent_actions":{"view_html":"https://pith.science/pith/HCMD5UFTK7MRHXF6FI2LTYUWL7","download_json":"https://pith.science/pith/HCMD5UFTK7MRHXF6FI2LTYUWL7.json","view_paper":"https://pith.science/paper/HCMD5UFT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.05334&json=true","fetch_graph":"https://pith.science/api/pith-number/HCMD5UFTK7MRHXF6FI2LTYUWL7/graph.json","fetch_events":"https://pith.science/api/pith-number/HCMD5UFTK7MRHXF6FI2LTYUWL7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HCMD5UFTK7MRHXF6FI2LTYUWL7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HCMD5UFTK7MRHXF6FI2LTYUWL7/action/storage_attestation","attest_author":"https://pith.science/pith/HCMD5UFTK7MRHXF6FI2LTYUWL7/action/author_attestation","sign_citation":"https://pith.science/pith/HCMD5UFTK7MRHXF6FI2LTYUWL7/action/citation_signature","submit_replication":"https://pith.science/pith/HCMD5UFTK7MRHXF6FI2LTYUWL7/action/replication_record"}},"created_at":"2026-05-18T00:07:57.508103+00:00","updated_at":"2026-05-18T00:07:57.508103+00:00"}