{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:G7BWW4IZRVAN7SWPQM53BFQZ5W","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":"0de730eead4f05e4e9253a23da8843f36082245a1fa557a8640e88e5c38610df","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-10-20T10:41:37Z","title_canon_sha256":"d01fd2ec83fb871ce7fe048a0392d30cf6c6db1545f305599bfe8f5134f96243"},"schema_version":"1.0","source":{"id":"2010.10181","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.10181","created_at":"2026-07-05T02:16:27Z"},{"alias_kind":"arxiv_version","alias_value":"2010.10181v3","created_at":"2026-07-05T02:16:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.10181","created_at":"2026-07-05T02:16:27Z"},{"alias_kind":"pith_short_12","alias_value":"G7BWW4IZRVAN","created_at":"2026-07-05T02:16:27Z"},{"alias_kind":"pith_short_16","alias_value":"G7BWW4IZRVAN7SWP","created_at":"2026-07-05T02:16:27Z"},{"alias_kind":"pith_short_8","alias_value":"G7BWW4IZ","created_at":"2026-07-05T02:16:27Z"}],"graph_snapshots":[{"event_id":"sha256:748859f5cd0e394506ef521e5275f2a5cd38520425a8572ac0c770092f2483e9","target":"graph","created_at":"2026-07-05T02:16:27Z","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/2010.10181/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Robust learning from noisy demonstrations is a practical but highly challenging problem in imitation learning. In this paper, we first theoretically show that robust imitation learning can be achieved by optimizing a classification risk with a symmetric loss. Based on this theoretical finding, we then propose a new imitation learning method that optimizes the classification risk by effectively combining pseudo-labeling with co-training. Unlike existing methods, our method does not require additional labels or strict assumptions about noise distributions. Experimental results on continuous-cont","authors_text":"Masashi Sugiyama, Nontawat Charoenphakdee, Voot Tangkaratt","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-10-20T10:41:37Z","title":"Robust Imitation Learning from Noisy Demonstrations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.10181","kind":"arxiv","version":3},"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:dcc9076e709b4f9fc5eedb125e357db5ef733471f03176ee851d4297906ecdf2","target":"record","created_at":"2026-07-05T02:16:27Z","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":"0de730eead4f05e4e9253a23da8843f36082245a1fa557a8640e88e5c38610df","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-10-20T10:41:37Z","title_canon_sha256":"d01fd2ec83fb871ce7fe048a0392d30cf6c6db1545f305599bfe8f5134f96243"},"schema_version":"1.0","source":{"id":"2010.10181","kind":"arxiv","version":3}},"canonical_sha256":"37c36b71198d40dfcacf833bb09619edab0caa4071536abfe14eb2a0b6bfacf0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"37c36b71198d40dfcacf833bb09619edab0caa4071536abfe14eb2a0b6bfacf0","first_computed_at":"2026-07-05T02:16:27.888192Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:16:27.888192Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XhwGkbqzaF9L0R5cr8+43sG4tMpO73e7+SZR2rTfTbdVJDHvnjvwp5Qefu6KIEfyN0vUe41TNnmoQc2cqAoMCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:16:27.888536Z","signed_message":"canonical_sha256_bytes"},"source_id":"2010.10181","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dcc9076e709b4f9fc5eedb125e357db5ef733471f03176ee851d4297906ecdf2","sha256:748859f5cd0e394506ef521e5275f2a5cd38520425a8572ac0c770092f2483e9"],"state_sha256":"890d712bb1fd380784e8211fb9c009894b2baa94b8fc2824965e28f9314a8cb9"}