{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:WRO3QXQIOIZXFUIMNEKWWN6OFJ","short_pith_number":"pith:WRO3QXQI","canonical_record":{"source":{"id":"1810.12161","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-10-29T14:42:04Z","cross_cats_sorted":["cs.LG","stat.CO","stat.ME"],"title_canon_sha256":"61a30c37250cdc6e740eb68a7819627e771e428600136aad1544e926a881776d","abstract_canon_sha256":"b2d4ba32bba4ea1351da5fdd3fc9bcabc1d37baba035321b18a4d9835d03ba13"},"schema_version":"1.0"},"canonical_sha256":"b45db85e08723372d10c69156b37ce2a5da0e9d84ebfe09fc6721ffb1dabc64d","source":{"kind":"arxiv","id":"1810.12161","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.12161","created_at":"2026-05-18T00:02:04Z"},{"alias_kind":"arxiv_version","alias_value":"1810.12161v1","created_at":"2026-05-18T00:02:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.12161","created_at":"2026-05-18T00:02:04Z"},{"alias_kind":"pith_short_12","alias_value":"WRO3QXQIOIZX","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"WRO3QXQIOIZXFUIM","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"WRO3QXQI","created_at":"2026-05-18T12:33:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:WRO3QXQIOIZXFUIMNEKWWN6OFJ","target":"record","payload":{"canonical_record":{"source":{"id":"1810.12161","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-10-29T14:42:04Z","cross_cats_sorted":["cs.LG","stat.CO","stat.ME"],"title_canon_sha256":"61a30c37250cdc6e740eb68a7819627e771e428600136aad1544e926a881776d","abstract_canon_sha256":"b2d4ba32bba4ea1351da5fdd3fc9bcabc1d37baba035321b18a4d9835d03ba13"},"schema_version":"1.0"},"canonical_sha256":"b45db85e08723372d10c69156b37ce2a5da0e9d84ebfe09fc6721ffb1dabc64d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:02:04.084025Z","signature_b64":"GeViza4z7O0zijzAXIGu/ieYDJV+LDmpYsACa5xmHTgrxxQ5FtHRR/xm0qDBuyuRQu+v2Cp319s+9DXLPi7TBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b45db85e08723372d10c69156b37ce2a5da0e9d84ebfe09fc6721ffb1dabc64d","last_reissued_at":"2026-05-18T00:02:04.083275Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:02:04.083275Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.12161","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":"2026-05-18T00:02:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+rl8cA8EHFVXQ0BERexynCLKrR7GWhUp5geu4EjuS5fzOMqn9qeHT0JqabTdSQH10oCeNOGClE8r0kWdCe/ZBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-26T01:04:10.706879Z"},"content_sha256":"0371990a6a515c97dda43a3061ab05acab2e2dd43fa74b173897375cc7c0e741","schema_version":"1.0","event_id":"sha256:0371990a6a515c97dda43a3061ab05acab2e2dd43fa74b173897375cc7c0e741"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:WRO3QXQIOIZXFUIMNEKWWN6OFJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Regularized Maximum Likelihood Estimation and Feature Selection in Mixtures-of-Experts Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.CO","stat.ME"],"primary_cat":"stat.ML","authors_text":"Bao-Tuyen Huynh, Faicel Chamroukhi","submitted_at":"2018-10-29T14:42:04Z","abstract_excerpt":"Mixture of Experts (MoE) are successful models for modeling heterogeneous data in many statistical learning problems including regression, clustering and classification. Generally fitted by maximum likelihood estimation via the well-known EM algorithm, their application to high-dimensional problems is still therefore challenging. We consider the problem of fitting and feature selection in MoE models, and propose a regularized maximum likelihood estimation approach that encourages sparse solutions for heterogeneous regression data models with potentially high-dimensional predictors. Unlike stat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.12161","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:02:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eEan/3uGx8q+bhqHPHp6CN7R0k2528tjdpIYLwDHAlFf0wZC0gvTVlgtlRkAbb3IzsWBTxpOeeAoHQxFah3SCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-26T01:04:10.707222Z"},"content_sha256":"700f63e09d5902fe647ad11c894e811a69820f68b458306c602d0267353f3336","schema_version":"1.0","event_id":"sha256:700f63e09d5902fe647ad11c894e811a69820f68b458306c602d0267353f3336"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WRO3QXQIOIZXFUIMNEKWWN6OFJ/bundle.json","state_url":"https://pith.science/pith/WRO3QXQIOIZXFUIMNEKWWN6OFJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WRO3QXQIOIZXFUIMNEKWWN6OFJ/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":"2026-06-26T01:04:10Z","links":{"resolver":"https://pith.science/pith/WRO3QXQIOIZXFUIMNEKWWN6OFJ","bundle":"https://pith.science/pith/WRO3QXQIOIZXFUIMNEKWWN6OFJ/bundle.json","state":"https://pith.science/pith/WRO3QXQIOIZXFUIMNEKWWN6OFJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WRO3QXQIOIZXFUIMNEKWWN6OFJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:WRO3QXQIOIZXFUIMNEKWWN6OFJ","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":"b2d4ba32bba4ea1351da5fdd3fc9bcabc1d37baba035321b18a4d9835d03ba13","cross_cats_sorted":["cs.LG","stat.CO","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-10-29T14:42:04Z","title_canon_sha256":"61a30c37250cdc6e740eb68a7819627e771e428600136aad1544e926a881776d"},"schema_version":"1.0","source":{"id":"1810.12161","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.12161","created_at":"2026-05-18T00:02:04Z"},{"alias_kind":"arxiv_version","alias_value":"1810.12161v1","created_at":"2026-05-18T00:02:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.12161","created_at":"2026-05-18T00:02:04Z"},{"alias_kind":"pith_short_12","alias_value":"WRO3QXQIOIZX","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"WRO3QXQIOIZXFUIM","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"WRO3QXQI","created_at":"2026-05-18T12:33:01Z"}],"graph_snapshots":[{"event_id":"sha256:700f63e09d5902fe647ad11c894e811a69820f68b458306c602d0267353f3336","target":"graph","created_at":"2026-05-18T00:02:04Z","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"},"paper":{"abstract_excerpt":"Mixture of Experts (MoE) are successful models for modeling heterogeneous data in many statistical learning problems including regression, clustering and classification. Generally fitted by maximum likelihood estimation via the well-known EM algorithm, their application to high-dimensional problems is still therefore challenging. We consider the problem of fitting and feature selection in MoE models, and propose a regularized maximum likelihood estimation approach that encourages sparse solutions for heterogeneous regression data models with potentially high-dimensional predictors. Unlike stat","authors_text":"Bao-Tuyen Huynh, Faicel Chamroukhi","cross_cats":["cs.LG","stat.CO","stat.ME"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-10-29T14:42:04Z","title":"Regularized Maximum Likelihood Estimation and Feature Selection in Mixtures-of-Experts Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.12161","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":"sha256:0371990a6a515c97dda43a3061ab05acab2e2dd43fa74b173897375cc7c0e741","target":"record","created_at":"2026-05-18T00:02:04Z","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":"b2d4ba32bba4ea1351da5fdd3fc9bcabc1d37baba035321b18a4d9835d03ba13","cross_cats_sorted":["cs.LG","stat.CO","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-10-29T14:42:04Z","title_canon_sha256":"61a30c37250cdc6e740eb68a7819627e771e428600136aad1544e926a881776d"},"schema_version":"1.0","source":{"id":"1810.12161","kind":"arxiv","version":1}},"canonical_sha256":"b45db85e08723372d10c69156b37ce2a5da0e9d84ebfe09fc6721ffb1dabc64d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b45db85e08723372d10c69156b37ce2a5da0e9d84ebfe09fc6721ffb1dabc64d","first_computed_at":"2026-05-18T00:02:04.083275Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:02:04.083275Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GeViza4z7O0zijzAXIGu/ieYDJV+LDmpYsACa5xmHTgrxxQ5FtHRR/xm0qDBuyuRQu+v2Cp319s+9DXLPi7TBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:02:04.084025Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.12161","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0371990a6a515c97dda43a3061ab05acab2e2dd43fa74b173897375cc7c0e741","sha256:700f63e09d5902fe647ad11c894e811a69820f68b458306c602d0267353f3336"],"state_sha256":"c141b25166ccdf4764d4da7e94fd6109d574416fd0262ab1fbe0eb00e4bc0866"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mqFUg9EWJU6q/nEF80qcCH+XmjbG7zLUJ28z9fvEN2qfoTt/4mdZOw5teleeY36H3Wo4RcbUOjdhumkb3mfJCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-26T01:04:10.709184Z","bundle_sha256":"985c62fec3e517dc92dc0be18d8d57358a706a8869301215f9692de8b8b3c6e7"}}