{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:3EKE6YQY3DD3J6FEHAZ3GY2CFA","short_pith_number":"pith:3EKE6YQY","canonical_record":{"source":{"id":"2006.10398","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-18T10:01:18Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"792a2a8065a4c48dfffa98924e8e7ac105b743ca2b9a1951819814f041542261","abstract_canon_sha256":"8876cc49223599cf81a7a5d9564f25d2b11580923b885100a6e41277a55a6163"},"schema_version":"1.0"},"canonical_sha256":"d9144f6218d8c7b4f8a43833b36342280361e49f7087ea65f0d4c2c7edfd6b21","source":{"kind":"arxiv","id":"2006.10398","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.10398","created_at":"2026-07-05T01:34:43Z"},{"alias_kind":"arxiv_version","alias_value":"2006.10398v1","created_at":"2026-07-05T01:34:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.10398","created_at":"2026-07-05T01:34:43Z"},{"alias_kind":"pith_short_12","alias_value":"3EKE6YQY3DD3","created_at":"2026-07-05T01:34:43Z"},{"alias_kind":"pith_short_16","alias_value":"3EKE6YQY3DD3J6FE","created_at":"2026-07-05T01:34:43Z"},{"alias_kind":"pith_short_8","alias_value":"3EKE6YQY","created_at":"2026-07-05T01:34:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:3EKE6YQY3DD3J6FEHAZ3GY2CFA","target":"record","payload":{"canonical_record":{"source":{"id":"2006.10398","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-18T10:01:18Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"792a2a8065a4c48dfffa98924e8e7ac105b743ca2b9a1951819814f041542261","abstract_canon_sha256":"8876cc49223599cf81a7a5d9564f25d2b11580923b885100a6e41277a55a6163"},"schema_version":"1.0"},"canonical_sha256":"d9144f6218d8c7b4f8a43833b36342280361e49f7087ea65f0d4c2c7edfd6b21","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:34:43.287747Z","signature_b64":"PylQN7CcDJt6M/5nQ7MnsqxGGky5Qpl+PEqdRG2VNOYJQZ6CYcx5sofp1v8ZE5U8KZauZYPic+Le7bYI1YN3Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d9144f6218d8c7b4f8a43833b36342280361e49f7087ea65f0d4c2c7edfd6b21","last_reissued_at":"2026-07-05T01:34:43.287352Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:34:43.287352Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2006.10398","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":"2026-07-05T01:34:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"p7t15zMyqJfZsJqtZnsZs1Pm+MGE99KCDiE5dXPVAaSHL7N/YdGCQmh4S63wWFSFXlRtUJrGWPhRKSFKDxBPBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T10:17:56.289509Z"},"content_sha256":"6513e850f06a53b58638248b4e79052ad8b4432392df79523c391d98848d5d29","schema_version":"1.0","event_id":"sha256:6513e850f06a53b58638248b4e79052ad8b4432392df79523c391d98848d5d29"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:3EKE6YQY3DD3J6FEHAZ3GY2CFA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Leveraging Model Inherent Variable Importance for Stable Online Feature Selection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Gjergji Kasneci, Johannes Haug, Klaus Broelemann, Martin Pawelczyk","submitted_at":"2020-06-18T10:01:18Z","abstract_excerpt":"Feature selection can be a crucial factor in obtaining robust and accurate predictions. Online feature selection models, however, operate under considerable restrictions; they need to efficiently extract salient input features based on a bounded set of observations, while enabling robust and accurate predictions. In this work, we introduce FIRES, a novel framework for online feature selection. The proposed feature weighting mechanism leverages the importance information inherent in the parameters of a predictive model. By treating model parameters as random variables, we can penalize features "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.10398","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/2006.10398/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":"2026-07-05T01:34:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"F/jAi2Q0AEILufqV8XGzYvaPW0JnQeE3A/Lj832LQVXwxEubt8hyU3gwKDWEGnW0kb6yqoGAnuOYJXqCWtzCDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T10:17:56.290007Z"},"content_sha256":"268d1812866de364199bf7224525e0b7f91dddf627879509a991dec356c823f7","schema_version":"1.0","event_id":"sha256:268d1812866de364199bf7224525e0b7f91dddf627879509a991dec356c823f7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3EKE6YQY3DD3J6FEHAZ3GY2CFA/bundle.json","state_url":"https://pith.science/pith/3EKE6YQY3DD3J6FEHAZ3GY2CFA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3EKE6YQY3DD3J6FEHAZ3GY2CFA/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":"2026-08-22T10:17:56Z","links":{"resolver":"https://pith.science/pith/3EKE6YQY3DD3J6FEHAZ3GY2CFA","bundle":"https://pith.science/pith/3EKE6YQY3DD3J6FEHAZ3GY2CFA/bundle.json","state":"https://pith.science/pith/3EKE6YQY3DD3J6FEHAZ3GY2CFA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3EKE6YQY3DD3J6FEHAZ3GY2CFA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:3EKE6YQY3DD3J6FEHAZ3GY2CFA","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":"8876cc49223599cf81a7a5d9564f25d2b11580923b885100a6e41277a55a6163","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-18T10:01:18Z","title_canon_sha256":"792a2a8065a4c48dfffa98924e8e7ac105b743ca2b9a1951819814f041542261"},"schema_version":"1.0","source":{"id":"2006.10398","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.10398","created_at":"2026-07-05T01:34:43Z"},{"alias_kind":"arxiv_version","alias_value":"2006.10398v1","created_at":"2026-07-05T01:34:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.10398","created_at":"2026-07-05T01:34:43Z"},{"alias_kind":"pith_short_12","alias_value":"3EKE6YQY3DD3","created_at":"2026-07-05T01:34:43Z"},{"alias_kind":"pith_short_16","alias_value":"3EKE6YQY3DD3J6FE","created_at":"2026-07-05T01:34:43Z"},{"alias_kind":"pith_short_8","alias_value":"3EKE6YQY","created_at":"2026-07-05T01:34:43Z"}],"graph_snapshots":[{"event_id":"sha256:268d1812866de364199bf7224525e0b7f91dddf627879509a991dec356c823f7","target":"graph","created_at":"2026-07-05T01:34:43Z","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/2006.10398/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Feature selection can be a crucial factor in obtaining robust and accurate predictions. Online feature selection models, however, operate under considerable restrictions; they need to efficiently extract salient input features based on a bounded set of observations, while enabling robust and accurate predictions. In this work, we introduce FIRES, a novel framework for online feature selection. The proposed feature weighting mechanism leverages the importance information inherent in the parameters of a predictive model. By treating model parameters as random variables, we can penalize features ","authors_text":"Gjergji Kasneci, Johannes Haug, Klaus Broelemann, Martin Pawelczyk","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-18T10:01:18Z","title":"Leveraging Model Inherent Variable Importance for Stable Online Feature Selection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.10398","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":"sha256:6513e850f06a53b58638248b4e79052ad8b4432392df79523c391d98848d5d29","target":"record","created_at":"2026-07-05T01:34:43Z","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":"8876cc49223599cf81a7a5d9564f25d2b11580923b885100a6e41277a55a6163","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-06-18T10:01:18Z","title_canon_sha256":"792a2a8065a4c48dfffa98924e8e7ac105b743ca2b9a1951819814f041542261"},"schema_version":"1.0","source":{"id":"2006.10398","kind":"arxiv","version":1}},"canonical_sha256":"d9144f6218d8c7b4f8a43833b36342280361e49f7087ea65f0d4c2c7edfd6b21","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d9144f6218d8c7b4f8a43833b36342280361e49f7087ea65f0d4c2c7edfd6b21","first_computed_at":"2026-07-05T01:34:43.287352Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:34:43.287352Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PylQN7CcDJt6M/5nQ7MnsqxGGky5Qpl+PEqdRG2VNOYJQZ6CYcx5sofp1v8ZE5U8KZauZYPic+Le7bYI1YN3Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:34:43.287747Z","signed_message":"canonical_sha256_bytes"},"source_id":"2006.10398","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6513e850f06a53b58638248b4e79052ad8b4432392df79523c391d98848d5d29","sha256:268d1812866de364199bf7224525e0b7f91dddf627879509a991dec356c823f7"],"state_sha256":"18d604c60ffca98a43eb3f0d9f9e70a33b74ec445466c9bed9e232e7cee74e20"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cAKHOgx8x8Vo1gfq09rOc/OxNqZXbbozKbRR8IwtE/GZd4miAvbFzj7GNtzw3Y0pTA9zQYO8q0+WwFjwwzgaDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-22T10:17:56.294577Z","bundle_sha256":"64ffe9039397f6d4a55052b82fb76c375346ecadfe3a75825dd86b57f20ad24e"}}