{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:5JMQMEUYJIQNTMZCOR4VADSYDU","short_pith_number":"pith:5JMQMEUY","schema_version":"1.0","canonical_sha256":"ea590612984a20d9b3227479500e581d0f90da9eb215ff7e7f0cc2d18f45e2c5","source":{"kind":"arxiv","id":"2508.04670","version":1},"attestation_state":"computed","paper":{"title":"Robustly Learning Monotone Single-Index Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.LG","authors_text":"Ilias Diakonikolas, Jelena Diakonikolas, Nikos Zarifis, Puqian Wang","submitted_at":"2025-08-06T17:37:06Z","abstract_excerpt":"We consider the basic problem of learning Single-Index Models with respect to the square loss under the Gaussian distribution in the presence of adversarial label noise. Our main contribution is the first computationally efficient algorithm for this learning task, achieving a constant factor approximation, that succeeds for the class of {\\em all} monotone activations with bounded moment of order $2 + \\zeta,$ for $\\zeta > 0.$ This class in particular includes all monotone Lipschitz functions and even discontinuous functions like (possibly biased) halfspaces. Prior work for the case of unknown a"},"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":"2508.04670","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-06T17:37:06Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"63d36614924ce525d2c2d2a11745235201db0afe5e08a398f51f13585724d87f","abstract_canon_sha256":"3926522c8d50cfebd1a3d23b9211aa60e840b815b929fda6bcb76119180280d2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:49:41.493377Z","signature_b64":"tCCYFpmC6JqoyE43YPT5shxgRivQSH3yGUKmSVDI3qZb3ZVNlKfvFw5Dg8hadB+T3zAzvVfGewEvSRMDkLDJCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ea590612984a20d9b3227479500e581d0f90da9eb215ff7e7f0cc2d18f45e2c5","last_reissued_at":"2026-07-05T11:49:41.492976Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:49:41.492976Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robustly Learning Monotone Single-Index Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.LG","authors_text":"Ilias Diakonikolas, Jelena Diakonikolas, Nikos Zarifis, Puqian Wang","submitted_at":"2025-08-06T17:37:06Z","abstract_excerpt":"We consider the basic problem of learning Single-Index Models with respect to the square loss under the Gaussian distribution in the presence of adversarial label noise. Our main contribution is the first computationally efficient algorithm for this learning task, achieving a constant factor approximation, that succeeds for the class of {\\em all} monotone activations with bounded moment of order $2 + \\zeta,$ for $\\zeta > 0.$ This class in particular includes all monotone Lipschitz functions and even discontinuous functions like (possibly biased) halfspaces. Prior work for the case of unknown a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.04670","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2508.04670/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":"2508.04670","created_at":"2026-07-05T11:49:41.493032+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.04670v1","created_at":"2026-07-05T11:49:41.493032+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.04670","created_at":"2026-07-05T11:49:41.493032+00:00"},{"alias_kind":"pith_short_12","alias_value":"5JMQMEUYJIQN","created_at":"2026-07-05T11:49:41.493032+00:00"},{"alias_kind":"pith_short_16","alias_value":"5JMQMEUYJIQNTMZC","created_at":"2026-07-05T11:49:41.493032+00:00"},{"alias_kind":"pith_short_8","alias_value":"5JMQMEUY","created_at":"2026-07-05T11:49:41.493032+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/5JMQMEUYJIQNTMZCOR4VADSYDU","json":"https://pith.science/pith/5JMQMEUYJIQNTMZCOR4VADSYDU.json","graph_json":"https://pith.science/api/pith-number/5JMQMEUYJIQNTMZCOR4VADSYDU/graph.json","events_json":"https://pith.science/api/pith-number/5JMQMEUYJIQNTMZCOR4VADSYDU/events.json","paper":"https://pith.science/paper/5JMQMEUY"},"agent_actions":{"view_html":"https://pith.science/pith/5JMQMEUYJIQNTMZCOR4VADSYDU","download_json":"https://pith.science/pith/5JMQMEUYJIQNTMZCOR4VADSYDU.json","view_paper":"https://pith.science/paper/5JMQMEUY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.04670&json=true","fetch_graph":"https://pith.science/api/pith-number/5JMQMEUYJIQNTMZCOR4VADSYDU/graph.json","fetch_events":"https://pith.science/api/pith-number/5JMQMEUYJIQNTMZCOR4VADSYDU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5JMQMEUYJIQNTMZCOR4VADSYDU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5JMQMEUYJIQNTMZCOR4VADSYDU/action/storage_attestation","attest_author":"https://pith.science/pith/5JMQMEUYJIQNTMZCOR4VADSYDU/action/author_attestation","sign_citation":"https://pith.science/pith/5JMQMEUYJIQNTMZCOR4VADSYDU/action/citation_signature","submit_replication":"https://pith.science/pith/5JMQMEUYJIQNTMZCOR4VADSYDU/action/replication_record"}},"created_at":"2026-07-05T11:49:41.493032+00:00","updated_at":"2026-07-05T11:49:41.493032+00:00"}