{"paper":{"title":"High-Dimensional Statistics: Reflections on Progress and Open Problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"High-dimensional statistics has evolved to tackle sophisticated problems in complex datasets by building connections across multiple mathematical and computational fields.","cross_cats":["stat.CO","stat.ME","stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Ali Shojaie, Anru Zhang, Arian Maleki, Chao Gao, Christos Thrampoulidis, Jason M. Klusowski, Po-Ling Loh, Rishabh Dudeja, Sivaraman Balakrishnan, Subhabrata Sen, Verena Zuber, Weijie Su","submitted_at":"2026-05-06T16:11:09Z","abstract_excerpt":"Over the past two decades, the field of high-dimensional statistics has experienced substantial progress, driven largely by technological advances that have dramatically reduced the cost and effort for data collection and storage across a broad range of domains, including biology, medicine, astronomy, and the social and environmental sciences. Modern datasets are increasingly complex, often exhibiting rich dependency, heterogeneity, and other features that challenge traditional statistical methods. In response, high-dimensional statistics has evolved to address more sophisticated estimation an"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Over the past two decades high-dimensional statistics has evolved to address sophisticated estimation and inference problems in complex datasets, fostering deep connections with optimization, concentration of measure, random matrix theory, information theory, and theoretical computer science.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the chosen representative advances and highlighted open problems accurately capture the field's most important developments and gaps without systematic omission of major threads.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A survey synthesizing representative advances, common themes, and open problems in high-dimensional statistics while pointing to key entry-point works.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"High-dimensional statistics has evolved to tackle sophisticated problems in complex datasets by building connections across multiple mathematical and computational fields.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"5d6a8dab7e6019158146b27fff52fa1ea0139e5ba02c25cfafda98039f2e7729"},"source":{"id":"2605.05076","kind":"arxiv","version":2},"verdict":{"id":"9b239ab1-096f-45fc-883e-b690070de8c4","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-08T15:30:14.838094Z","strongest_claim":"Over the past two decades high-dimensional statistics has evolved to address sophisticated estimation and inference problems in complex datasets, fostering deep connections with optimization, concentration of measure, random matrix theory, information theory, and theoretical computer science.","one_line_summary":"A survey synthesizing representative advances, common themes, and open problems in high-dimensional statistics while pointing to key entry-point works.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the chosen representative advances and highlighted open problems accurately capture the field's most important developments and gaps without systematic omission of major threads.","pith_extraction_headline":"High-dimensional statistics has evolved to tackle sophisticated problems in complex datasets by building connections across multiple mathematical and computational fields."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.05076/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-20T10:37:55.543888Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T21:31:19.658440Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T13:51:36.934512Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"777e17fd7b262208a69fe5a25248029e4b6fd366219d948bbe08e19ea7c7a5e2"},"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"}