{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:3XRXMO7465K6YFCQ3FO5ZTJMBS","short_pith_number":"pith:3XRXMO74","canonical_record":{"source":{"id":"1209.1557","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2012-09-07T14:46:49Z","cross_cats_sorted":["cs.LG","math.OC"],"title_canon_sha256":"8acd336b81a3d9e7c004e10d4a57edf56dffff864b35b5fed610963dd4a36558","abstract_canon_sha256":"51f748734be8bbb9caec8202b7d13d7c6233cbe3c8ab78e392cc166e978bf79d"},"schema_version":"1.0"},"canonical_sha256":"dde3763bfcf755ec1450d95ddccd2c0cbd11fa0714535460f68fd1a1a593a88b","source":{"kind":"arxiv","id":"1209.1557","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1209.1557","created_at":"2026-05-18T01:18:48Z"},{"alias_kind":"arxiv_version","alias_value":"1209.1557v4","created_at":"2026-05-18T01:18:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1209.1557","created_at":"2026-05-18T01:18:48Z"},{"alias_kind":"pith_short_12","alias_value":"3XRXMO7465K6","created_at":"2026-05-18T12:26:53Z"},{"alias_kind":"pith_short_16","alias_value":"3XRXMO7465K6YFCQ","created_at":"2026-05-18T12:26:53Z"},{"alias_kind":"pith_short_8","alias_value":"3XRXMO74","created_at":"2026-05-18T12:26:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:3XRXMO7465K6YFCQ3FO5ZTJMBS","target":"record","payload":{"canonical_record":{"source":{"id":"1209.1557","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2012-09-07T14:46:49Z","cross_cats_sorted":["cs.LG","math.OC"],"title_canon_sha256":"8acd336b81a3d9e7c004e10d4a57edf56dffff864b35b5fed610963dd4a36558","abstract_canon_sha256":"51f748734be8bbb9caec8202b7d13d7c6233cbe3c8ab78e392cc166e978bf79d"},"schema_version":"1.0"},"canonical_sha256":"dde3763bfcf755ec1450d95ddccd2c0cbd11fa0714535460f68fd1a1a593a88b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:18:48.154728Z","signature_b64":"6i70hPvvfIC/hlSiudCFwUNQQMATS+D1aL9NevivTqdueosHIYWf9BaK7hTcKXUWaZIAj9ULNJIAWcsAv8VWAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dde3763bfcf755ec1450d95ddccd2c0cbd11fa0714535460f68fd1a1a593a88b","last_reissued_at":"2026-05-18T01:18:48.153907Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:18:48.153907Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1209.1557","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:18:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Oh5jB1hcF6NG9YeWxf8i7WJZnUDGMG1PaLc5H+CD4rhH2KIEDT73+ud812kXj4KZN2ppDovrh6eU5Wogg+dSCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T03:58:33.093469Z"},"content_sha256":"539142bceb12c90c77ad2629901bb5f00ceb32f82b5e9e3cc5e12450f9bb7b31","schema_version":"1.0","event_id":"sha256:539142bceb12c90c77ad2629901bb5f00ceb32f82b5e9e3cc5e12450f9bb7b31"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:3XRXMO7465K6YFCQ3FO5ZTJMBS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Model-Based Sparsity via Projected Gradient Descent","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.OC"],"primary_cat":"stat.ML","authors_text":"Bhiksha Raj, Petros T. Boufounos, Sohail Bahmani","submitted_at":"2012-09-07T14:46:49Z","abstract_excerpt":"Several convex formulation methods have been proposed previously for statistical estimation with structured sparsity as the prior. These methods often require a carefully tuned regularization parameter, often a cumbersome or heuristic exercise. Furthermore, the estimate that these methods produce might not belong to the desired sparsity model, albeit accurately approximating the true parameter. Therefore, greedy-type algorithms could often be more desirable in estimating structured-sparse parameters. So far, these greedy methods have mostly focused on linear statistical models. In this paper w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1209.1557","kind":"arxiv","version":4},"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":"2026-05-18T01:18:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ytU8TlkX6NqQ3UWx2yrmUiJ2oJa9ZMczZvI2bTQlQfoMy6iEY69l+mtjJ+4MZJUvWYjxK1ujF9W8pGNr2MlJCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T03:58:33.093819Z"},"content_sha256":"2daaaa55ef295e4a9fbe8bdc433564d3ad917c5f8ff3b0b327b954d94f296b4e","schema_version":"1.0","event_id":"sha256:2daaaa55ef295e4a9fbe8bdc433564d3ad917c5f8ff3b0b327b954d94f296b4e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3XRXMO7465K6YFCQ3FO5ZTJMBS/bundle.json","state_url":"https://pith.science/pith/3XRXMO7465K6YFCQ3FO5ZTJMBS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3XRXMO7465K6YFCQ3FO5ZTJMBS/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":"2026-05-31T03:58:33Z","links":{"resolver":"https://pith.science/pith/3XRXMO7465K6YFCQ3FO5ZTJMBS","bundle":"https://pith.science/pith/3XRXMO7465K6YFCQ3FO5ZTJMBS/bundle.json","state":"https://pith.science/pith/3XRXMO7465K6YFCQ3FO5ZTJMBS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3XRXMO7465K6YFCQ3FO5ZTJMBS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:3XRXMO7465K6YFCQ3FO5ZTJMBS","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":"51f748734be8bbb9caec8202b7d13d7c6233cbe3c8ab78e392cc166e978bf79d","cross_cats_sorted":["cs.LG","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2012-09-07T14:46:49Z","title_canon_sha256":"8acd336b81a3d9e7c004e10d4a57edf56dffff864b35b5fed610963dd4a36558"},"schema_version":"1.0","source":{"id":"1209.1557","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1209.1557","created_at":"2026-05-18T01:18:48Z"},{"alias_kind":"arxiv_version","alias_value":"1209.1557v4","created_at":"2026-05-18T01:18:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1209.1557","created_at":"2026-05-18T01:18:48Z"},{"alias_kind":"pith_short_12","alias_value":"3XRXMO7465K6","created_at":"2026-05-18T12:26:53Z"},{"alias_kind":"pith_short_16","alias_value":"3XRXMO7465K6YFCQ","created_at":"2026-05-18T12:26:53Z"},{"alias_kind":"pith_short_8","alias_value":"3XRXMO74","created_at":"2026-05-18T12:26:53Z"}],"graph_snapshots":[{"event_id":"sha256:2daaaa55ef295e4a9fbe8bdc433564d3ad917c5f8ff3b0b327b954d94f296b4e","target":"graph","created_at":"2026-05-18T01:18:48Z","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":"Several convex formulation methods have been proposed previously for statistical estimation with structured sparsity as the prior. These methods often require a carefully tuned regularization parameter, often a cumbersome or heuristic exercise. Furthermore, the estimate that these methods produce might not belong to the desired sparsity model, albeit accurately approximating the true parameter. Therefore, greedy-type algorithms could often be more desirable in estimating structured-sparse parameters. So far, these greedy methods have mostly focused on linear statistical models. In this paper w","authors_text":"Bhiksha Raj, Petros T. Boufounos, Sohail Bahmani","cross_cats":["cs.LG","math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2012-09-07T14:46:49Z","title":"Learning Model-Based Sparsity via Projected Gradient Descent"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1209.1557","kind":"arxiv","version":4},"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":"sha256:539142bceb12c90c77ad2629901bb5f00ceb32f82b5e9e3cc5e12450f9bb7b31","target":"record","created_at":"2026-05-18T01:18:48Z","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":"51f748734be8bbb9caec8202b7d13d7c6233cbe3c8ab78e392cc166e978bf79d","cross_cats_sorted":["cs.LG","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2012-09-07T14:46:49Z","title_canon_sha256":"8acd336b81a3d9e7c004e10d4a57edf56dffff864b35b5fed610963dd4a36558"},"schema_version":"1.0","source":{"id":"1209.1557","kind":"arxiv","version":4}},"canonical_sha256":"dde3763bfcf755ec1450d95ddccd2c0cbd11fa0714535460f68fd1a1a593a88b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dde3763bfcf755ec1450d95ddccd2c0cbd11fa0714535460f68fd1a1a593a88b","first_computed_at":"2026-05-18T01:18:48.153907Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:18:48.153907Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6i70hPvvfIC/hlSiudCFwUNQQMATS+D1aL9NevivTqdueosHIYWf9BaK7hTcKXUWaZIAj9ULNJIAWcsAv8VWAw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:18:48.154728Z","signed_message":"canonical_sha256_bytes"},"source_id":"1209.1557","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:539142bceb12c90c77ad2629901bb5f00ceb32f82b5e9e3cc5e12450f9bb7b31","sha256:2daaaa55ef295e4a9fbe8bdc433564d3ad917c5f8ff3b0b327b954d94f296b4e"],"state_sha256":"3304092fe27a4ad24b6631930a2378e9478cc899f7f60db84fb0820046d74eab"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g+DyhaGz88SnVWhwasCtTumvIGkRrxyrJTKHlN1j+VWqlaCzg2KabE5zFSnjFfU+eyDhrki+TDHwjPN6EXpfBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T03:58:33.096054Z","bundle_sha256":"9393fa955354ba97225c238d67cc0cb95a4afb5d8086bc4813f2c2db681b0091"}}