<?xml version="1.0" encoding="UTF-8"?><rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcq="http://purl.org/dc/terms/"><records count="1" morepages="false" start="1" end="1"><record rownumber="1"><dc:product_type>Conference Paper</dc:product_type><dc:title>Neural Network-based Estimation of the MMSE</dc:title><dc:creator>Diaz, Mario; Kairouz, Peter; Liao, Jiachun; Sankar, Lalitha</dc:creator><dc:corporate_author/><dc:editor>null</dc:editor><dc:description>The minimum mean-square error (MMSE) achievable by optimal estimation of a random variable S given another random variable T is of much interest in a variety of statistical contexts. Motivated by a growing interest in auditing machine learning models for unintended information leakage, we propose a neural network-based estimator of this MMSE. We derive a lower bound for the MMSE based on the proposed estimator and the Barron constant associated with the conditional expectation of S given T . Since the latter is typically unknown in practice, we derive a general bound for the Barron constant that produces order optimal estimates for canonical distribution models.</dc:description><dc:publisher/><dc:date>2021-10-01</dc:date><dc:nsf_par_id>10297195</dc:nsf_par_id><dc:journal_name>International Symposium on Information Theory</dc:journal_name><dc:journal_volume/><dc:journal_issue/><dc:page_range_or_elocation>1023 to 1028</dc:page_range_or_elocation><dc:issn/><dc:isbn/><dc:doi>https://doi.org/10.1109/ISIT45174.2021.9518063</dc:doi><dcq:identifierAwardId>1815361; 2031799; 1901243; 2007688</dcq:identifierAwardId><dc:subject/><dc:version_number/><dc:location/><dc:rights/><dc:institution/><dc:sponsoring_org>National Science Foundation</dc:sponsoring_org></record></records></rdf:RDF>