{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:KP3GJ4CRWE6XHIO632RSL3KV5T","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":"92ecbe39afeb6849ee13b2daf98ec1d2c488ff1f062cb5ab09087308a273c13b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-02-24T17:28:12Z","title_canon_sha256":"22f4b5ab17ae132f69498ea915bc23a7deb9a0116e8adc8abee4bde91a6744d7"},"schema_version":"1.0","source":{"id":"2602.21132","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.21132","created_at":"2026-06-08T01:05:09Z"},{"alias_kind":"arxiv_version","alias_value":"2602.21132v2","created_at":"2026-06-08T01:05:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.21132","created_at":"2026-06-08T01:05:09Z"},{"alias_kind":"pith_short_12","alias_value":"KP3GJ4CRWE6X","created_at":"2026-06-08T01:05:09Z"},{"alias_kind":"pith_short_16","alias_value":"KP3GJ4CRWE6XHIO6","created_at":"2026-06-08T01:05:09Z"},{"alias_kind":"pith_short_8","alias_value":"KP3GJ4CR","created_at":"2026-06-08T01:05:09Z"}],"graph_snapshots":[{"event_id":"sha256:c9219ea5f8f07c2f0f65a5b6ed065d923892cc529e91eb41171ea22fdae47c00","target":"graph","created_at":"2026-06-08T01:05:09Z","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/2602.21132/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"High-dimensional datasets are frequently subject to contamination by outliers and heavy-tailed noise, which can severely bias standard regularized estimators like the Lasso. While Maximum Mean Discrepancy (MMD) has recently been introduced as a ``universal'' framework for robust regression, its application to high-dimensional Generalized Linear Models (GLMs) remains largely unexplored, particularly regarding variable selection. In this paper, we propose a penalized MMD framework for robust estimation and feature selection in GLMs. We introduce an $\\ell_1$-penalized MMD objective and develop tw","authors_text":"Lulu Kang, Xiaoning Kang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-02-24T17:28:12Z","title":"Robust and Sparse Generalized Linear Models for High-Dimensional Data via Maximum Mean Discrepancy"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.21132","kind":"arxiv","version":2},"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:b35ffa8a7d8c986bb5f9a20edde3d76b06e73c21f858c7bec545ea4638a34c83","target":"record","created_at":"2026-06-08T01:05:09Z","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":"92ecbe39afeb6849ee13b2daf98ec1d2c488ff1f062cb5ab09087308a273c13b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-02-24T17:28:12Z","title_canon_sha256":"22f4b5ab17ae132f69498ea915bc23a7deb9a0116e8adc8abee4bde91a6744d7"},"schema_version":"1.0","source":{"id":"2602.21132","kind":"arxiv","version":2}},"canonical_sha256":"53f664f051b13d73a1dedea325ed55ecdd0c4e069f778d44431ef749362020bb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"53f664f051b13d73a1dedea325ed55ecdd0c4e069f778d44431ef749362020bb","first_computed_at":"2026-06-08T01:05:09.198669Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-08T01:05:09.198669Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QEs9wVnAeUzUi9Uj7ySSwkkOaRlYw2ppiIN647xsoUYLSoIsXJ3KUb1QhNa+sjj7pMugLLSbnJmGzv3hNMEqDQ==","signature_status":"signed_v1","signed_at":"2026-06-08T01:05:09.199773Z","signed_message":"canonical_sha256_bytes"},"source_id":"2602.21132","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b35ffa8a7d8c986bb5f9a20edde3d76b06e73c21f858c7bec545ea4638a34c83","sha256:c9219ea5f8f07c2f0f65a5b6ed065d923892cc529e91eb41171ea22fdae47c00"],"state_sha256":"eef45c1fab91dc9cdedb1d9ee47ae65f9f4e45c48af5402d2ced26de29431193"}