{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:NXICV5N5UKVAPU5PEVQZRTPRYQ","short_pith_number":"pith:NXICV5N5","canonical_record":{"source":{"id":"2404.16328","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-04-25T04:29:25Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c4618ea20fd8ac2cac3299bffe4c7e7681f5f412eaadfe4e108a6e885c9482e5","abstract_canon_sha256":"5a12d41dbaf4015b0b2f8229ebcd47595a1d3a9913e6a53d618bdea1bda6f6b3"},"schema_version":"1.0"},"canonical_sha256":"6dd02af5bda2aa07d3af256198cdf1c431b5fca28fd3ef34a0b74528b0696942","source":{"kind":"arxiv","id":"2404.16328","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.16328","created_at":"2026-07-05T08:12:04Z"},{"alias_kind":"arxiv_version","alias_value":"2404.16328v1","created_at":"2026-07-05T08:12:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.16328","created_at":"2026-07-05T08:12:04Z"},{"alias_kind":"pith_short_12","alias_value":"NXICV5N5UKVA","created_at":"2026-07-05T08:12:04Z"},{"alias_kind":"pith_short_16","alias_value":"NXICV5N5UKVAPU5P","created_at":"2026-07-05T08:12:04Z"},{"alias_kind":"pith_short_8","alias_value":"NXICV5N5","created_at":"2026-07-05T08:12:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:NXICV5N5UKVAPU5PEVQZRTPRYQ","target":"record","payload":{"canonical_record":{"source":{"id":"2404.16328","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-04-25T04:29:25Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c4618ea20fd8ac2cac3299bffe4c7e7681f5f412eaadfe4e108a6e885c9482e5","abstract_canon_sha256":"5a12d41dbaf4015b0b2f8229ebcd47595a1d3a9913e6a53d618bdea1bda6f6b3"},"schema_version":"1.0"},"canonical_sha256":"6dd02af5bda2aa07d3af256198cdf1c431b5fca28fd3ef34a0b74528b0696942","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:12:04.783227Z","signature_b64":"ocORBgSMxrqDhjKAOe/gf7gZcy5yfm1Oh1TtDu54wQqzWn9FUDFa33BtYqZhEA94Oc7O/9I4t869Xvfw3vs8AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6dd02af5bda2aa07d3af256198cdf1c431b5fca28fd3ef34a0b74528b0696942","last_reissued_at":"2026-07-05T08:12:04.782791Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:12:04.782791Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2404.16328","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":"2026-07-05T08:12:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LzX4kNxUynLqgJ+TMKb5yzDS71dSMtAaW7iyYnqI8nZtxP9/FFaIrd0UEgIH5z/8hGD2YURiUnpa5tfEAxq4Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T14:05:08.566309Z"},"content_sha256":"e16cb98cd7ea9ff6185717828f0dbf88c95bec8bb79bcc5c9f1e7b5a008acb42","schema_version":"1.0","event_id":"sha256:e16cb98cd7ea9ff6185717828f0dbf88c95bec8bb79bcc5c9f1e7b5a008acb42"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:NXICV5N5UKVAPU5PEVQZRTPRYQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Distributionally Robust Safe Screening","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Hiroyuki Hanada, Ichiro Takeuchi, Lee Hanju, Noriaki Hashimoto, Satoshi Akahane, Shinya Kojima, Taro Murayama, Tatsuya Aoyama, Tomonari Tanaka, Yoshito Okura, Yu Inatsu","submitted_at":"2024-04-25T04:29:25Z","abstract_excerpt":"In this study, we propose a method Distributionally Robust Safe Screening (DRSS), for identifying unnecessary samples and features within a DR covariate shift setting. This method effectively combines DR learning, a paradigm aimed at enhancing model robustness against variations in data distribution, with safe screening (SS), a sparse optimization technique designed to identify irrelevant samples and features prior to model training. The core concept of the DRSS method involves reformulating the DR covariate-shift problem as a weighted empirical risk minimization problem, where the weights are"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.16328","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/2404.16328/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:12:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"S/8dLbQ2q9dqEkNkUviExAzVWDl0yGhIVLmPimjjqCoz1L6h2GvXnsLNioAHhmaV4j2fFaeEUqrSRwI00i5nDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T14:05:08.566649Z"},"content_sha256":"b7e8542e96b85960f22e429f2e5fbb33af61377e8de36d5ce5fd56231a0f4dc9","schema_version":"1.0","event_id":"sha256:b7e8542e96b85960f22e429f2e5fbb33af61377e8de36d5ce5fd56231a0f4dc9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NXICV5N5UKVAPU5PEVQZRTPRYQ/bundle.json","state_url":"https://pith.science/pith/NXICV5N5UKVAPU5PEVQZRTPRYQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NXICV5N5UKVAPU5PEVQZRTPRYQ/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":"2026-08-14T14:05:08Z","links":{"resolver":"https://pith.science/pith/NXICV5N5UKVAPU5PEVQZRTPRYQ","bundle":"https://pith.science/pith/NXICV5N5UKVAPU5PEVQZRTPRYQ/bundle.json","state":"https://pith.science/pith/NXICV5N5UKVAPU5PEVQZRTPRYQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NXICV5N5UKVAPU5PEVQZRTPRYQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:NXICV5N5UKVAPU5PEVQZRTPRYQ","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":"5a12d41dbaf4015b0b2f8229ebcd47595a1d3a9913e6a53d618bdea1bda6f6b3","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-04-25T04:29:25Z","title_canon_sha256":"c4618ea20fd8ac2cac3299bffe4c7e7681f5f412eaadfe4e108a6e885c9482e5"},"schema_version":"1.0","source":{"id":"2404.16328","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.16328","created_at":"2026-07-05T08:12:04Z"},{"alias_kind":"arxiv_version","alias_value":"2404.16328v1","created_at":"2026-07-05T08:12:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.16328","created_at":"2026-07-05T08:12:04Z"},{"alias_kind":"pith_short_12","alias_value":"NXICV5N5UKVA","created_at":"2026-07-05T08:12:04Z"},{"alias_kind":"pith_short_16","alias_value":"NXICV5N5UKVAPU5P","created_at":"2026-07-05T08:12:04Z"},{"alias_kind":"pith_short_8","alias_value":"NXICV5N5","created_at":"2026-07-05T08:12:04Z"}],"graph_snapshots":[{"event_id":"sha256:b7e8542e96b85960f22e429f2e5fbb33af61377e8de36d5ce5fd56231a0f4dc9","target":"graph","created_at":"2026-07-05T08:12:04Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2404.16328/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this study, we propose a method Distributionally Robust Safe Screening (DRSS), for identifying unnecessary samples and features within a DR covariate shift setting. This method effectively combines DR learning, a paradigm aimed at enhancing model robustness against variations in data distribution, with safe screening (SS), a sparse optimization technique designed to identify irrelevant samples and features prior to model training. The core concept of the DRSS method involves reformulating the DR covariate-shift problem as a weighted empirical risk minimization problem, where the weights are","authors_text":"Hiroyuki Hanada, Ichiro Takeuchi, Lee Hanju, Noriaki Hashimoto, Satoshi Akahane, Shinya Kojima, Taro Murayama, Tatsuya Aoyama, Tomonari Tanaka, Yoshito Okura, Yu Inatsu","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-04-25T04:29:25Z","title":"Distributionally Robust Safe Screening"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.16328","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:e16cb98cd7ea9ff6185717828f0dbf88c95bec8bb79bcc5c9f1e7b5a008acb42","target":"record","created_at":"2026-07-05T08:12:04Z","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":"5a12d41dbaf4015b0b2f8229ebcd47595a1d3a9913e6a53d618bdea1bda6f6b3","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-04-25T04:29:25Z","title_canon_sha256":"c4618ea20fd8ac2cac3299bffe4c7e7681f5f412eaadfe4e108a6e885c9482e5"},"schema_version":"1.0","source":{"id":"2404.16328","kind":"arxiv","version":1}},"canonical_sha256":"6dd02af5bda2aa07d3af256198cdf1c431b5fca28fd3ef34a0b74528b0696942","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6dd02af5bda2aa07d3af256198cdf1c431b5fca28fd3ef34a0b74528b0696942","first_computed_at":"2026-07-05T08:12:04.782791Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:12:04.782791Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ocORBgSMxrqDhjKAOe/gf7gZcy5yfm1Oh1TtDu54wQqzWn9FUDFa33BtYqZhEA94Oc7O/9I4t869Xvfw3vs8AA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:12:04.783227Z","signed_message":"canonical_sha256_bytes"},"source_id":"2404.16328","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e16cb98cd7ea9ff6185717828f0dbf88c95bec8bb79bcc5c9f1e7b5a008acb42","sha256:b7e8542e96b85960f22e429f2e5fbb33af61377e8de36d5ce5fd56231a0f4dc9"],"state_sha256":"6a124a345ec9484d79c81c720251c8304b62132c2a570f188640799813ce344a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+6s6eXOQRECoqn6sPwhR1/KfgZkeuA6WFcaLdZM1O/GJ3WxR7qxOPE3HOuNi5RS9dAhUQHecwgGSjxaIyXAtDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T14:05:08.569749Z","bundle_sha256":"03f2af69ed14a077e7821c2bdbaa7e58e574acc530afd6f1cfbaaa41278af23b"}}