{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:IVANPUZ67KFTNSFWW4DRBLQS2G","short_pith_number":"pith:IVANPUZ6","canonical_record":{"source":{"id":"1701.07895","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-01-26T22:43:20Z","cross_cats_sorted":["cs.IT","math.IT","stat.ML"],"title_canon_sha256":"2e0e2df6d8e3c00c6e9901763ed5fb49ea1030587a29598c15ab979dfbdf10ec","abstract_canon_sha256":"267439ec839b8206bda403d40497a5dc9fc2b5a129d20b53ac84481e82d555cf"},"schema_version":"1.0"},"canonical_sha256":"4540d7d33efa8b36c8b6b70710ae12d19fe825b120a42332ad07adda9f74c2f9","source":{"kind":"arxiv","id":"1701.07895","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1701.07895","created_at":"2026-05-18T00:00:37Z"},{"alias_kind":"arxiv_version","alias_value":"1701.07895v2","created_at":"2026-05-18T00:00:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.07895","created_at":"2026-05-18T00:00:37Z"},{"alias_kind":"pith_short_12","alias_value":"IVANPUZ67KFT","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_16","alias_value":"IVANPUZ67KFTNSFW","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_8","alias_value":"IVANPUZ6","created_at":"2026-05-18T12:31:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:IVANPUZ67KFTNSFWW4DRBLQS2G","target":"record","payload":{"canonical_record":{"source":{"id":"1701.07895","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-01-26T22:43:20Z","cross_cats_sorted":["cs.IT","math.IT","stat.ML"],"title_canon_sha256":"2e0e2df6d8e3c00c6e9901763ed5fb49ea1030587a29598c15ab979dfbdf10ec","abstract_canon_sha256":"267439ec839b8206bda403d40497a5dc9fc2b5a129d20b53ac84481e82d555cf"},"schema_version":"1.0"},"canonical_sha256":"4540d7d33efa8b36c8b6b70710ae12d19fe825b120a42332ad07adda9f74c2f9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:37.234160Z","signature_b64":"1Wnk/rnvtcKBHEaZav7z8lUCIyg8dM4DQaGEZdMdrMok8jlb2o/X9//dCnwQaCQG46gJTDWMDQ2Hgwzd/UpPAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4540d7d33efa8b36c8b6b70710ae12d19fe825b120a42332ad07adda9f74c2f9","last_reissued_at":"2026-05-18T00:00:37.233569Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:37.233569Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1701.07895","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:00:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R+3akveIondS2YR7Pn1vO27JlVYxmxZqQ1GqP+3axuby6iAePaMHF8JdIzNsGm5clenn2J21U7sZiliBwiViAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T07:56:27.570298Z"},"content_sha256":"27d6eb9df68838b2e2844397356c41ab296f19e2d2c0b07b0d2b6555d8fd88c3","schema_version":"1.0","event_id":"sha256:27d6eb9df68838b2e2844397356c41ab296f19e2d2c0b07b0d2b6555d8fd88c3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:IVANPUZ67KFTNSFWW4DRBLQS2G","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Information Theoretic Limits for Linear Prediction with Graph-Structured Sparsity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT","stat.ML"],"primary_cat":"cs.LG","authors_text":"Adarsh Barik, Jean Honorio, Mohit Tawarmalani","submitted_at":"2017-01-26T22:43:20Z","abstract_excerpt":"We analyze the necessary number of samples for sparse vector recovery in a noisy linear prediction setup. This model includes problems such as linear regression and classification. We focus on structured graph models. In particular, we prove that sufficient number of samples for the weighted graph model proposed by Hegde and others is also necessary. We use the Fano's inequality on well constructed ensembles as our main tool in establishing information theoretic lower bounds."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.07895","kind":"arxiv","version":2},"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":"2026-05-18T00:00:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+/L/KOARrVT99WKUBUwlTS7DXyddPx6Fm8nDw6dsLQre4XDwL180V9144KkgqB0ELqCb/mrjTlHDfdPTWenWAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T07:56:27.571083Z"},"content_sha256":"8c4b432b278a3791cb72152ef7fa0b9a1ac7cd02eb06f509a29641b5146b729e","schema_version":"1.0","event_id":"sha256:8c4b432b278a3791cb72152ef7fa0b9a1ac7cd02eb06f509a29641b5146b729e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IVANPUZ67KFTNSFWW4DRBLQS2G/bundle.json","state_url":"https://pith.science/pith/IVANPUZ67KFTNSFWW4DRBLQS2G/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IVANPUZ67KFTNSFWW4DRBLQS2G/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":"2026-05-31T07:56:27Z","links":{"resolver":"https://pith.science/pith/IVANPUZ67KFTNSFWW4DRBLQS2G","bundle":"https://pith.science/pith/IVANPUZ67KFTNSFWW4DRBLQS2G/bundle.json","state":"https://pith.science/pith/IVANPUZ67KFTNSFWW4DRBLQS2G/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IVANPUZ67KFTNSFWW4DRBLQS2G/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:IVANPUZ67KFTNSFWW4DRBLQS2G","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":"267439ec839b8206bda403d40497a5dc9fc2b5a129d20b53ac84481e82d555cf","cross_cats_sorted":["cs.IT","math.IT","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-01-26T22:43:20Z","title_canon_sha256":"2e0e2df6d8e3c00c6e9901763ed5fb49ea1030587a29598c15ab979dfbdf10ec"},"schema_version":"1.0","source":{"id":"1701.07895","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1701.07895","created_at":"2026-05-18T00:00:37Z"},{"alias_kind":"arxiv_version","alias_value":"1701.07895v2","created_at":"2026-05-18T00:00:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.07895","created_at":"2026-05-18T00:00:37Z"},{"alias_kind":"pith_short_12","alias_value":"IVANPUZ67KFT","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_16","alias_value":"IVANPUZ67KFTNSFW","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_8","alias_value":"IVANPUZ6","created_at":"2026-05-18T12:31:21Z"}],"graph_snapshots":[{"event_id":"sha256:8c4b432b278a3791cb72152ef7fa0b9a1ac7cd02eb06f509a29641b5146b729e","target":"graph","created_at":"2026-05-18T00:00:37Z","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":"We analyze the necessary number of samples for sparse vector recovery in a noisy linear prediction setup. This model includes problems such as linear regression and classification. We focus on structured graph models. In particular, we prove that sufficient number of samples for the weighted graph model proposed by Hegde and others is also necessary. We use the Fano's inequality on well constructed ensembles as our main tool in establishing information theoretic lower bounds.","authors_text":"Adarsh Barik, Jean Honorio, Mohit Tawarmalani","cross_cats":["cs.IT","math.IT","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-01-26T22:43:20Z","title":"Information Theoretic Limits for Linear Prediction with Graph-Structured Sparsity"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.07895","kind":"arxiv","version":2},"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":"sha256:27d6eb9df68838b2e2844397356c41ab296f19e2d2c0b07b0d2b6555d8fd88c3","target":"record","created_at":"2026-05-18T00:00:37Z","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":"267439ec839b8206bda403d40497a5dc9fc2b5a129d20b53ac84481e82d555cf","cross_cats_sorted":["cs.IT","math.IT","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-01-26T22:43:20Z","title_canon_sha256":"2e0e2df6d8e3c00c6e9901763ed5fb49ea1030587a29598c15ab979dfbdf10ec"},"schema_version":"1.0","source":{"id":"1701.07895","kind":"arxiv","version":2}},"canonical_sha256":"4540d7d33efa8b36c8b6b70710ae12d19fe825b120a42332ad07adda9f74c2f9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4540d7d33efa8b36c8b6b70710ae12d19fe825b120a42332ad07adda9f74c2f9","first_computed_at":"2026-05-18T00:00:37.233569Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:00:37.233569Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1Wnk/rnvtcKBHEaZav7z8lUCIyg8dM4DQaGEZdMdrMok8jlb2o/X9//dCnwQaCQG46gJTDWMDQ2Hgwzd/UpPAw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:00:37.234160Z","signed_message":"canonical_sha256_bytes"},"source_id":"1701.07895","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:27d6eb9df68838b2e2844397356c41ab296f19e2d2c0b07b0d2b6555d8fd88c3","sha256:8c4b432b278a3791cb72152ef7fa0b9a1ac7cd02eb06f509a29641b5146b729e"],"state_sha256":"1dde3bab786bf1d127dfb4ede391ff55b158f75aca093527d19d35481817b864"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TiIbrXxQ7SxyBUseBj6COzpOf2hf2FJY9bOUnyndvM9yLOXApOtsE05IZAgQx1qWVZtTVOzZO6AxWODdKN3LCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T07:56:27.576301Z","bundle_sha256":"9b2426b021fe31210174728149c0490595e5ee4f891b4dd5b90ecd94242641d0"}}