{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:76XHYNFF7GXWW3GO2OC6SPGY3S","short_pith_number":"pith:76XHYNFF","canonical_record":{"source":{"id":"1803.01570","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-03-05T09:30:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"481aa9d26f6140ae11583e7812c22151837c99158cc2b6353684c851b1d22d1a","abstract_canon_sha256":"9118b5af77a7144b8a5a11789ba58ad2b0b49c103961b19219a26e24072b925a"},"schema_version":"1.0"},"canonical_sha256":"ffae7c34a5f9af6b6cced385e93cd8dc9d83417f7283c9e92f122a71693d04d5","source":{"kind":"arxiv","id":"1803.01570","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.01570","created_at":"2026-05-18T00:22:00Z"},{"alias_kind":"arxiv_version","alias_value":"1803.01570v1","created_at":"2026-05-18T00:22:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.01570","created_at":"2026-05-18T00:22:00Z"},{"alias_kind":"pith_short_12","alias_value":"76XHYNFF7GXW","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"76XHYNFF7GXWW3GO","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"76XHYNFF","created_at":"2026-05-18T12:32:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:76XHYNFF7GXWW3GO2OC6SPGY3S","target":"record","payload":{"canonical_record":{"source":{"id":"1803.01570","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-03-05T09:30:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"481aa9d26f6140ae11583e7812c22151837c99158cc2b6353684c851b1d22d1a","abstract_canon_sha256":"9118b5af77a7144b8a5a11789ba58ad2b0b49c103961b19219a26e24072b925a"},"schema_version":"1.0"},"canonical_sha256":"ffae7c34a5f9af6b6cced385e93cd8dc9d83417f7283c9e92f122a71693d04d5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:22:00.230987Z","signature_b64":"VlEJSrph7Ym8rgYBKsPwX9evlinUp2gNLlHJRYmJvRkIBNeyGC/sspBWJZ4dxYVA4Op9I1Ho6yOirEnN7j0MDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ffae7c34a5f9af6b6cced385e93cd8dc9d83417f7283c9e92f122a71693d04d5","last_reissued_at":"2026-05-18T00:22:00.230295Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:22:00.230295Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1803.01570","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":"2026-05-18T00:22:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"66H3ZBzDBjKkXl6QdPCx6JjezkKGZzqXt1pBcpgPSG/KKYGKknA3KFlXvaWCqSGkTtNXK8py1mZj6nkcVlp5BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T18:45:03.577026Z"},"content_sha256":"36e6dc7fde9cfca7b9f5aa5fdc5a8fa79ab91f94380c2c1348581b3792766ffc","schema_version":"1.0","event_id":"sha256:36e6dc7fde9cfca7b9f5aa5fdc5a8fa79ab91f94380c2c1348581b3792766ffc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:76XHYNFF7GXWW3GO2OC6SPGY3S","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adversarial Extreme Multi-label Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Bernhard Sch\\\"olkopf, Rohit Babbar","submitted_at":"2018-03-05T09:30:46Z","abstract_excerpt":"The goal in extreme multi-label classification is to learn a classifier which can assign a small subset of relevant labels to an instance from an extremely large set of target labels. Datasets in extreme classification exhibit a long tail of labels which have small number of positive training instances. In this work, we pose the learning task in extreme classification with large number of tail-labels as learning in the presence of adversarial perturbations. This view motivates a robust optimization framework and equivalence to a corresponding regularized objective.\n  Under the proposed robustn"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.01570","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":""},"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":"2026-05-18T00:22:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qC8pNEROe+tLNXlfsrCJSoPp/2rV7NHag3SbBgmT+0dj4PzcqPmZ5jNKpkcqWflb0kmuWb4o7kfP1uAvp59ODw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T18:45:03.577807Z"},"content_sha256":"0bb3bf71a610263080dc0a99fd02ff2d75755b2f89a27ffb4e109f6467ab72f0","schema_version":"1.0","event_id":"sha256:0bb3bf71a610263080dc0a99fd02ff2d75755b2f89a27ffb4e109f6467ab72f0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/76XHYNFF7GXWW3GO2OC6SPGY3S/bundle.json","state_url":"https://pith.science/pith/76XHYNFF7GXWW3GO2OC6SPGY3S/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/76XHYNFF7GXWW3GO2OC6SPGY3S/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":"2026-06-07T18:45:03Z","links":{"resolver":"https://pith.science/pith/76XHYNFF7GXWW3GO2OC6SPGY3S","bundle":"https://pith.science/pith/76XHYNFF7GXWW3GO2OC6SPGY3S/bundle.json","state":"https://pith.science/pith/76XHYNFF7GXWW3GO2OC6SPGY3S/state.json","well_known_bundle":"https://pith.science/.well-known/pith/76XHYNFF7GXWW3GO2OC6SPGY3S/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:76XHYNFF7GXWW3GO2OC6SPGY3S","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":"9118b5af77a7144b8a5a11789ba58ad2b0b49c103961b19219a26e24072b925a","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-03-05T09:30:46Z","title_canon_sha256":"481aa9d26f6140ae11583e7812c22151837c99158cc2b6353684c851b1d22d1a"},"schema_version":"1.0","source":{"id":"1803.01570","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.01570","created_at":"2026-05-18T00:22:00Z"},{"alias_kind":"arxiv_version","alias_value":"1803.01570v1","created_at":"2026-05-18T00:22:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.01570","created_at":"2026-05-18T00:22:00Z"},{"alias_kind":"pith_short_12","alias_value":"76XHYNFF7GXW","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"76XHYNFF7GXWW3GO","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"76XHYNFF","created_at":"2026-05-18T12:32:11Z"}],"graph_snapshots":[{"event_id":"sha256:0bb3bf71a610263080dc0a99fd02ff2d75755b2f89a27ffb4e109f6467ab72f0","target":"graph","created_at":"2026-05-18T00:22:00Z","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"},"paper":{"abstract_excerpt":"The goal in extreme multi-label classification is to learn a classifier which can assign a small subset of relevant labels to an instance from an extremely large set of target labels. Datasets in extreme classification exhibit a long tail of labels which have small number of positive training instances. In this work, we pose the learning task in extreme classification with large number of tail-labels as learning in the presence of adversarial perturbations. This view motivates a robust optimization framework and equivalence to a corresponding regularized objective.\n  Under the proposed robustn","authors_text":"Bernhard Sch\\\"olkopf, Rohit Babbar","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-03-05T09:30:46Z","title":"Adversarial Extreme Multi-label Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.01570","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":"sha256:36e6dc7fde9cfca7b9f5aa5fdc5a8fa79ab91f94380c2c1348581b3792766ffc","target":"record","created_at":"2026-05-18T00:22:00Z","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":"9118b5af77a7144b8a5a11789ba58ad2b0b49c103961b19219a26e24072b925a","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-03-05T09:30:46Z","title_canon_sha256":"481aa9d26f6140ae11583e7812c22151837c99158cc2b6353684c851b1d22d1a"},"schema_version":"1.0","source":{"id":"1803.01570","kind":"arxiv","version":1}},"canonical_sha256":"ffae7c34a5f9af6b6cced385e93cd8dc9d83417f7283c9e92f122a71693d04d5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ffae7c34a5f9af6b6cced385e93cd8dc9d83417f7283c9e92f122a71693d04d5","first_computed_at":"2026-05-18T00:22:00.230295Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:22:00.230295Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VlEJSrph7Ym8rgYBKsPwX9evlinUp2gNLlHJRYmJvRkIBNeyGC/sspBWJZ4dxYVA4Op9I1Ho6yOirEnN7j0MDg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:22:00.230987Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.01570","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:36e6dc7fde9cfca7b9f5aa5fdc5a8fa79ab91f94380c2c1348581b3792766ffc","sha256:0bb3bf71a610263080dc0a99fd02ff2d75755b2f89a27ffb4e109f6467ab72f0"],"state_sha256":"4ec5731dcffbe2b2acebca3b3ec22c5eeda32ee7235d62f1d2df35c5271da692"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hzPR+0Tx+uV+IEVL1CUrPGTQzORQqenIoFzUVyL05DEsuMKIiiNHl3uUunsUJgw4qy+PEAB/SOsXSn4Gx9gQDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T18:45:03.581615Z","bundle_sha256":"a16b22358afdeeb44542537e46c8ab80dd982bd7f13dce8a83717906d61938a9"}}