{"paper":{"title":"Learning transformed product distributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Constantinos Daskalakis, Ilias Diakonikolas, Rocco A. Servedio","submitted_at":"2011-03-03T02:46:51Z","abstract_excerpt":"We consider the problem of learning an unknown product distribution $X$ over $\\{0,1\\}^n$ using samples $f(X)$ where $f$ is a \\emph{known} transformation function. Each choice of a transformation function $f$ specifies a learning problem in this framework.\n  Information-theoretic arguments show that for every transformation function $f$ the corresponding learning problem can be solved to accuracy $\\eps$, using $\\tilde{O}(n/\\eps^2)$ examples, by a generic algorithm whose running time may be exponential in $n.$ We show that this learning problem can be computationally intractable even for constan"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1103.0598","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"}