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Our protocol is completely distributed and able to cope with the time-varying, unpredictable, and noisy nature of interagent communication, and intermittent noisy measurements of $\\mu$. Our main result bounds the learning speed of our protocol in terms of the size and combinatorial features of the (time-varying) networks connecting the nodes.<\/jats:p>","DOI":"10.1137\/120891010","type":"journal-article","created":{"date-parts":[[2015,1,6]],"date-time":"2015-01-06T12:30:16Z","timestamp":1420547416000},"page":"1-29","source":"Crossref","is-referenced-by-count":15,"title":["Cooperative Learning in Multiagent Systems from Intermittent Measurements"],"prefix":"10.1137","volume":"53","author":[{"given":"Naomi Ehrich","family":"Leonard","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex","family":"Olshevsky","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"351","published-online":{"date-parts":[[2015,1,6]]},"reference":[{"key":"atypb1","doi-asserted-by":"publisher","DOI":"10.1093\/restud\/rdr004"},{"key":"atypb2","doi-asserted-by":"crossref","unstructured":"D. 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