{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:6HINWU3JYP2TV4QCUYQWJMRKMC","short_pith_number":"pith:6HINWU3J","canonical_record":{"source":{"id":"1710.03971","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-10-11T09:12:45Z","cross_cats_sorted":["cs.LG","math.NA"],"title_canon_sha256":"f70826a3e51bde9ca36b4d7daabe459ee920872c4bd3aa1b18edea0feb5df221","abstract_canon_sha256":"47eed2497c0f12d141cdb5ed4b2ac84eeb09bd7c8549137403dd89cb062f7e2f"},"schema_version":"1.0"},"canonical_sha256":"f1d0db5369c3f53af202a62164b22a608182707b72c841f4cbff732d0479739c","source":{"kind":"arxiv","id":"1710.03971","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.03971","created_at":"2026-05-18T00:33:05Z"},{"alias_kind":"arxiv_version","alias_value":"1710.03971v1","created_at":"2026-05-18T00:33:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.03971","created_at":"2026-05-18T00:33:05Z"},{"alias_kind":"pith_short_12","alias_value":"6HINWU3JYP2T","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_16","alias_value":"6HINWU3JYP2TV4QC","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_8","alias_value":"6HINWU3J","created_at":"2026-05-18T12:31:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:6HINWU3JYP2TV4QCUYQWJMRKMC","target":"record","payload":{"canonical_record":{"source":{"id":"1710.03971","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-10-11T09:12:45Z","cross_cats_sorted":["cs.LG","math.NA"],"title_canon_sha256":"f70826a3e51bde9ca36b4d7daabe459ee920872c4bd3aa1b18edea0feb5df221","abstract_canon_sha256":"47eed2497c0f12d141cdb5ed4b2ac84eeb09bd7c8549137403dd89cb062f7e2f"},"schema_version":"1.0"},"canonical_sha256":"f1d0db5369c3f53af202a62164b22a608182707b72c841f4cbff732d0479739c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:33:05.441879Z","signature_b64":"wfwlbGO05LrqMT/42LvAYQXvR3Ex2YZ4QKW5P0jdamXANcpkdPWVFh4G7KCXyVCRgdvtHAbZPGtmn2+snRLIDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f1d0db5369c3f53af202a62164b22a608182707b72c841f4cbff732d0479739c","last_reissued_at":"2026-05-18T00:33:05.441417Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:33:05.441417Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1710.03971","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":"2026-05-18T00:33:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JZdaITN7TtYv+wcdfQxHwhdwmy4bkO+/0x6Gy2u4x7vz8xYFNIqFozRHPJgzs611W6ft6aKCcoMsBFExh8vIAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T13:04:45.775328Z"},"content_sha256":"4504a562217553898deb4d840766c67e4c1e172542c916c942ee9dee8fbe4d8a","schema_version":"1.0","event_id":"sha256:4504a562217553898deb4d840766c67e4c1e172542c916c942ee9dee8fbe4d8a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:6HINWU3JYP2TV4QCUYQWJMRKMC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adaptive multi-penalty regularization based on a generalized Lasso path","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.NA"],"primary_cat":"stat.ML","authors_text":"Markus Grasmair, Timo Klock, Valeriya Naumova","submitted_at":"2017-10-11T09:12:45Z","abstract_excerpt":"For many algorithms, parameter tuning remains a challenging and critical task, which becomes tedious and infeasible in a multi-parameter setting. Multi-penalty regularization, successfully used for solving undetermined sparse regression of problems of unmixing type where signal and noise are additively mixed, is one of such examples. In this paper, we propose a novel algorithmic framework for an adaptive parameter choice in multi-penalty regularization with a focus on the correct support recovery. Building upon the theory of regularization paths and algorithms for single-penalty functionals, w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.03971","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":"2026-05-18T00:33:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BSmntEs4xoNZcs58dneo2hRnPQocV5BIeItuXrpHmdtQlhMAeaA/Fpp4tL/5zi2xlWOfX16Df4Pkh7aUwqO2Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T13:04:45.775985Z"},"content_sha256":"0ca48a55144bb9acc10a9bb9f9c5a89882883db2dca848f4f6ed2ec7f1a69e54","schema_version":"1.0","event_id":"sha256:0ca48a55144bb9acc10a9bb9f9c5a89882883db2dca848f4f6ed2ec7f1a69e54"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6HINWU3JYP2TV4QCUYQWJMRKMC/bundle.json","state_url":"https://pith.science/pith/6HINWU3JYP2TV4QCUYQWJMRKMC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6HINWU3JYP2TV4QCUYQWJMRKMC/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":"2026-05-27T13:04:45Z","links":{"resolver":"https://pith.science/pith/6HINWU3JYP2TV4QCUYQWJMRKMC","bundle":"https://pith.science/pith/6HINWU3JYP2TV4QCUYQWJMRKMC/bundle.json","state":"https://pith.science/pith/6HINWU3JYP2TV4QCUYQWJMRKMC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6HINWU3JYP2TV4QCUYQWJMRKMC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:6HINWU3JYP2TV4QCUYQWJMRKMC","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":"47eed2497c0f12d141cdb5ed4b2ac84eeb09bd7c8549137403dd89cb062f7e2f","cross_cats_sorted":["cs.LG","math.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-10-11T09:12:45Z","title_canon_sha256":"f70826a3e51bde9ca36b4d7daabe459ee920872c4bd3aa1b18edea0feb5df221"},"schema_version":"1.0","source":{"id":"1710.03971","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.03971","created_at":"2026-05-18T00:33:05Z"},{"alias_kind":"arxiv_version","alias_value":"1710.03971v1","created_at":"2026-05-18T00:33:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.03971","created_at":"2026-05-18T00:33:05Z"},{"alias_kind":"pith_short_12","alias_value":"6HINWU3JYP2T","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_16","alias_value":"6HINWU3JYP2TV4QC","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_8","alias_value":"6HINWU3J","created_at":"2026-05-18T12:31:03Z"}],"graph_snapshots":[{"event_id":"sha256:0ca48a55144bb9acc10a9bb9f9c5a89882883db2dca848f4f6ed2ec7f1a69e54","target":"graph","created_at":"2026-05-18T00:33:05Z","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":"For many algorithms, parameter tuning remains a challenging and critical task, which becomes tedious and infeasible in a multi-parameter setting. Multi-penalty regularization, successfully used for solving undetermined sparse regression of problems of unmixing type where signal and noise are additively mixed, is one of such examples. In this paper, we propose a novel algorithmic framework for an adaptive parameter choice in multi-penalty regularization with a focus on the correct support recovery. Building upon the theory of regularization paths and algorithms for single-penalty functionals, w","authors_text":"Markus Grasmair, Timo Klock, Valeriya Naumova","cross_cats":["cs.LG","math.NA"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-10-11T09:12:45Z","title":"Adaptive multi-penalty regularization based on a generalized Lasso path"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.03971","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":"sha256:4504a562217553898deb4d840766c67e4c1e172542c916c942ee9dee8fbe4d8a","target":"record","created_at":"2026-05-18T00:33:05Z","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":"47eed2497c0f12d141cdb5ed4b2ac84eeb09bd7c8549137403dd89cb062f7e2f","cross_cats_sorted":["cs.LG","math.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-10-11T09:12:45Z","title_canon_sha256":"f70826a3e51bde9ca36b4d7daabe459ee920872c4bd3aa1b18edea0feb5df221"},"schema_version":"1.0","source":{"id":"1710.03971","kind":"arxiv","version":1}},"canonical_sha256":"f1d0db5369c3f53af202a62164b22a608182707b72c841f4cbff732d0479739c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f1d0db5369c3f53af202a62164b22a608182707b72c841f4cbff732d0479739c","first_computed_at":"2026-05-18T00:33:05.441417Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:33:05.441417Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wfwlbGO05LrqMT/42LvAYQXvR3Ex2YZ4QKW5P0jdamXANcpkdPWVFh4G7KCXyVCRgdvtHAbZPGtmn2+snRLIDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:33:05.441879Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.03971","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4504a562217553898deb4d840766c67e4c1e172542c916c942ee9dee8fbe4d8a","sha256:0ca48a55144bb9acc10a9bb9f9c5a89882883db2dca848f4f6ed2ec7f1a69e54"],"state_sha256":"d0cbc675a58b83ca9c7fceee9a15d44556bbcd355199ef9a3d03f65c6538d07a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"C2qLbuZAlUtgrtp4GoAUu80Rm+s15RFt1EYI2zyhpTPkG28cJdFRvWLe8hWF6gYdMYCZba1XINnTTy8KI1s2Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T13:04:45.779464Z","bundle_sha256":"822ced968929b5d9a2d352f4b12fcb1b2722b1e11a5312ed190892c7189332b4"}}