{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:II3UK2DTRSIMJGSVDVIG2YK2A5","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":"f1fb7c3b2b6f0da67b200b3eb58a693b2db5d0043335126490c3c7ba76396137","cross_cats_sorted":["cs.IT","cs.LG","eess.SP","math.IT","stat.ML","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2021-10-18T17:51:14Z","title_canon_sha256":"deea532ee7db4eeda36e2ee5b9a5f7b59dd2e7b841ca2b8152fb1823e657b1f2"},"schema_version":"1.0","source":{"id":"2110.09502","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.09502","created_at":"2026-07-05T03:23:28Z"},{"alias_kind":"arxiv_version","alias_value":"2110.09502v1","created_at":"2026-07-05T03:23:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.09502","created_at":"2026-07-05T03:23:28Z"},{"alias_kind":"pith_short_12","alias_value":"II3UK2DTRSIM","created_at":"2026-07-05T03:23:28Z"},{"alias_kind":"pith_short_16","alias_value":"II3UK2DTRSIMJGSV","created_at":"2026-07-05T03:23:28Z"},{"alias_kind":"pith_short_8","alias_value":"II3UK2DT","created_at":"2026-07-05T03:23:28Z"}],"graph_snapshots":[{"event_id":"sha256:cc109bee05b256472fd4ba3e0446c3b3f0c49bf9e7346cb77bf0819357f9e203","target":"graph","created_at":"2026-07-05T03:23:28Z","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/2110.09502/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"An evolving line of machine learning works observe empirical evidence that suggests interpolating estimators -- the ones that achieve zero training error -- may not necessarily be harmful. This paper pursues theoretical understanding for an important type of interpolators: the minimum $\\ell_{1}$-norm interpolator, which is motivated by the observation that several learning algorithms favor low $\\ell_1$-norm solutions in the over-parameterized regime. Concretely, we consider the noisy sparse regression model under Gaussian design, focusing on linear sparsity and high-dimensional asymptotics (so","authors_text":"Yue Li, Yuting Wei","cross_cats":["cs.IT","cs.LG","eess.SP","math.IT","stat.ML","stat.TH"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2021-10-18T17:51:14Z","title":"Minimum $\\ell_{1}$-norm interpolators: Precise asymptotics and multiple descent"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.09502","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:e6b5422a2e4fb8b3eab30b1163e1dfabc98b821d5bfc51041e2adb2e16284eaf","target":"record","created_at":"2026-07-05T03:23:28Z","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":"f1fb7c3b2b6f0da67b200b3eb58a693b2db5d0043335126490c3c7ba76396137","cross_cats_sorted":["cs.IT","cs.LG","eess.SP","math.IT","stat.ML","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2021-10-18T17:51:14Z","title_canon_sha256":"deea532ee7db4eeda36e2ee5b9a5f7b59dd2e7b841ca2b8152fb1823e657b1f2"},"schema_version":"1.0","source":{"id":"2110.09502","kind":"arxiv","version":1}},"canonical_sha256":"42374568738c90c49a551d506d615a077354a948dc780f2f03733c7deaa22d66","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"42374568738c90c49a551d506d615a077354a948dc780f2f03733c7deaa22d66","first_computed_at":"2026-07-05T03:23:28.753979Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:23:28.753979Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9Z2CJM5zTuHWNDINbLLFYvnlEyJG2b2VkGlL1UtrV12YdntL9dhEytS0tfK5De+gtXTwKj3vmoFQSlg7URDpBA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:23:28.754389Z","signed_message":"canonical_sha256_bytes"},"source_id":"2110.09502","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e6b5422a2e4fb8b3eab30b1163e1dfabc98b821d5bfc51041e2adb2e16284eaf","sha256:cc109bee05b256472fd4ba3e0446c3b3f0c49bf9e7346cb77bf0819357f9e203"],"state_sha256":"e241359bd270ce3dfd7d56c6dd907ddde131ac2087a9e73a98207afd7c10199f"}