{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:WJKZRQFPROJSFVTW7QGJEFVALT","short_pith_number":"pith:WJKZRQFP","canonical_record":{"source":{"id":"1602.04287","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-02-13T04:18:03Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"fd946db4e07b3d0fd1fc9c1e4c5cde3b01eb291f2f770af8b9e6698118cc4ed1","abstract_canon_sha256":"9ef405087fab5e8c23a2ac73a947fc4475e1ab3dae7d6557fe9804b810da0b9d"},"schema_version":"1.0"},"canonical_sha256":"b25598c0af8b9322d676fc0c9216a05cf10f47bf9d182061fc75f03f762007c4","source":{"kind":"arxiv","id":"1602.04287","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1602.04287","created_at":"2026-05-18T01:20:51Z"},{"alias_kind":"arxiv_version","alias_value":"1602.04287v1","created_at":"2026-05-18T01:20:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.04287","created_at":"2026-05-18T01:20:51Z"},{"alias_kind":"pith_short_12","alias_value":"WJKZRQFPROJS","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_16","alias_value":"WJKZRQFPROJSFVTW","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_8","alias_value":"WJKZRQFP","created_at":"2026-05-18T12:30:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:WJKZRQFPROJSFVTW7QGJEFVALT","target":"record","payload":{"canonical_record":{"source":{"id":"1602.04287","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-02-13T04:18:03Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"fd946db4e07b3d0fd1fc9c1e4c5cde3b01eb291f2f770af8b9e6698118cc4ed1","abstract_canon_sha256":"9ef405087fab5e8c23a2ac73a947fc4475e1ab3dae7d6557fe9804b810da0b9d"},"schema_version":"1.0"},"canonical_sha256":"b25598c0af8b9322d676fc0c9216a05cf10f47bf9d182061fc75f03f762007c4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:20:51.314087Z","signature_b64":"KU2Db0HySsfFIGS9iUkB6uxuLRH3hNB1oJHNjzOtUZQmpuq19qUjgt9TCX+sAl5drGbBItYEB/2D5fnHfEEeAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b25598c0af8b9322d676fc0c9216a05cf10f47bf9d182061fc75f03f762007c4","last_reissued_at":"2026-05-18T01:20:51.313579Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:20:51.313579Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1602.04287","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":"2026-05-18T01:20:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KpAysRe8paIQA6+irdZY1Cjh0P6YdX3BrHYe/l9Y93TyyrJMhiYDTXTraPhhRdNYeSafNOfZPl57fJckxBGjAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T08:30:14.111062Z"},"content_sha256":"4abc25e328fe95b3aec241f798720750667e0bbf8c9586be0fba4c093bcdc9cb","schema_version":"1.0","event_id":"sha256:4abc25e328fe95b3aec241f798720750667e0bbf8c9586be0fba4c093bcdc9cb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:WJKZRQFPROJSFVTW7QGJEFVALT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Minimax Theory for Adaptive Data Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Jing Lei, Stephen E. Fienberg, Yu-Xiang Wang","submitted_at":"2016-02-13T04:18:03Z","abstract_excerpt":"In adaptive data analysis, the user makes a sequence of queries on the data, where at each step the choice of query may depend on the results in previous steps. The releases are often randomized in order to reduce overfitting for such adaptively chosen queries. In this paper, we propose a minimax framework for adaptive data analysis. Assuming Gaussianity of queries, we establish the first sharp minimax lower bound on the squared error in the order of $O(\\frac{\\sqrt{k}\\sigma^2}{n})$, where $k$ is the number of queries asked, and $\\sigma^2/n$ is the ordinary signal-to-noise ratio for a single qu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.04287","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":"2026-05-18T01:20:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jRtfMkjz1mD2V4to+wtfmoH4g7Xvi4Oj1TBNl3rZCPSU6wOKDZU7RyWpzgaCYy5VvzU3oHuWaUoF63ykQ7ltCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T08:30:14.111741Z"},"content_sha256":"e538df0282e5962e4f7be51c6c8e25221639b319828c1e888c45fae76795ab0a","schema_version":"1.0","event_id":"sha256:e538df0282e5962e4f7be51c6c8e25221639b319828c1e888c45fae76795ab0a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WJKZRQFPROJSFVTW7QGJEFVALT/bundle.json","state_url":"https://pith.science/pith/WJKZRQFPROJSFVTW7QGJEFVALT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WJKZRQFPROJSFVTW7QGJEFVALT/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":"2026-06-05T08:30:14Z","links":{"resolver":"https://pith.science/pith/WJKZRQFPROJSFVTW7QGJEFVALT","bundle":"https://pith.science/pith/WJKZRQFPROJSFVTW7QGJEFVALT/bundle.json","state":"https://pith.science/pith/WJKZRQFPROJSFVTW7QGJEFVALT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WJKZRQFPROJSFVTW7QGJEFVALT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:WJKZRQFPROJSFVTW7QGJEFVALT","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":"9ef405087fab5e8c23a2ac73a947fc4475e1ab3dae7d6557fe9804b810da0b9d","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-02-13T04:18:03Z","title_canon_sha256":"fd946db4e07b3d0fd1fc9c1e4c5cde3b01eb291f2f770af8b9e6698118cc4ed1"},"schema_version":"1.0","source":{"id":"1602.04287","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1602.04287","created_at":"2026-05-18T01:20:51Z"},{"alias_kind":"arxiv_version","alias_value":"1602.04287v1","created_at":"2026-05-18T01:20:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.04287","created_at":"2026-05-18T01:20:51Z"},{"alias_kind":"pith_short_12","alias_value":"WJKZRQFPROJS","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_16","alias_value":"WJKZRQFPROJSFVTW","created_at":"2026-05-18T12:30:48Z"},{"alias_kind":"pith_short_8","alias_value":"WJKZRQFP","created_at":"2026-05-18T12:30:48Z"}],"graph_snapshots":[{"event_id":"sha256:e538df0282e5962e4f7be51c6c8e25221639b319828c1e888c45fae76795ab0a","target":"graph","created_at":"2026-05-18T01:20:51Z","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":"In adaptive data analysis, the user makes a sequence of queries on the data, where at each step the choice of query may depend on the results in previous steps. The releases are often randomized in order to reduce overfitting for such adaptively chosen queries. In this paper, we propose a minimax framework for adaptive data analysis. Assuming Gaussianity of queries, we establish the first sharp minimax lower bound on the squared error in the order of $O(\\frac{\\sqrt{k}\\sigma^2}{n})$, where $k$ is the number of queries asked, and $\\sigma^2/n$ is the ordinary signal-to-noise ratio for a single qu","authors_text":"Jing Lei, Stephen E. Fienberg, Yu-Xiang Wang","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-02-13T04:18:03Z","title":"A Minimax Theory for Adaptive Data Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.04287","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":"sha256:4abc25e328fe95b3aec241f798720750667e0bbf8c9586be0fba4c093bcdc9cb","target":"record","created_at":"2026-05-18T01:20:51Z","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":"9ef405087fab5e8c23a2ac73a947fc4475e1ab3dae7d6557fe9804b810da0b9d","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-02-13T04:18:03Z","title_canon_sha256":"fd946db4e07b3d0fd1fc9c1e4c5cde3b01eb291f2f770af8b9e6698118cc4ed1"},"schema_version":"1.0","source":{"id":"1602.04287","kind":"arxiv","version":1}},"canonical_sha256":"b25598c0af8b9322d676fc0c9216a05cf10f47bf9d182061fc75f03f762007c4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b25598c0af8b9322d676fc0c9216a05cf10f47bf9d182061fc75f03f762007c4","first_computed_at":"2026-05-18T01:20:51.313579Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:20:51.313579Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KU2Db0HySsfFIGS9iUkB6uxuLRH3hNB1oJHNjzOtUZQmpuq19qUjgt9TCX+sAl5drGbBItYEB/2D5fnHfEEeAg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:20:51.314087Z","signed_message":"canonical_sha256_bytes"},"source_id":"1602.04287","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4abc25e328fe95b3aec241f798720750667e0bbf8c9586be0fba4c093bcdc9cb","sha256:e538df0282e5962e4f7be51c6c8e25221639b319828c1e888c45fae76795ab0a"],"state_sha256":"fa8c97557ed7ade3c94ece68aceb1e672aff8cf381949ed32d73eba67266ee5f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"p75vEO1qltOn4yb85JHet4FJ6Zoi9Zq33IEND/L09sfRxqVLR2WBVZAhnE4YNXZlv7WmBssANysKYtx2VIA1CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T08:30:14.114851Z","bundle_sha256":"db37b557eec76ed661ac54239dd6590c97958357d9f0d4a5ddecf63bd39fbb2e"}}