{"625121":{"#nid":"625121","#data":{"type":"event","title":"TRIAD Lecture Series by Yuxin Chen from Princeton (5\/5)","body":[{"value":"\u003Cp\u003EThis is one of a series of talks that are given by Professor Chen. The full list of his talks is as follows:\u003Cbr \/\u003E\r\nWednesday, August 28, 2019; 11:00 am - 12:00 pm; Groseclose 402\u003Cbr \/\u003E\r\nThursday, August 29, 2019; 11:00 am - 12:00 pm; Groseclose 402\u003Cbr \/\u003E\r\nTuesday, September 3, 2019; 11:00 am - 12:00 pm; Main - Executive Education Room 228\u003Cbr \/\u003E\r\nWednesday, September 4, 2019; 11:00 am - 12:00 pm; Main - Executive Education Room 228\u003Cbr \/\u003E\r\nThursday, September 5, 2019; 11:00 am - 12:00 pm; Groseclose 402\u003C\/p\u003E\r\n\r\n\u003Cp\u003ECheck https:\/\/triad.gatech.edu\/events for more information.\u0026nbsp;\u003Cbr \/\u003E\r\nFor location information, please check https:\/\/isye.gatech.edu\/about\/maps-directions\/isye-building-complex\u003C\/p\u003E\r\n\r\n\u003Cp\u003ETitle of this talk: Inference and Uncertainty Quantification for Noise Matrix Completion\u003C\/p\u003E\r\n\r\n\u003Cp\u003EAbstract:\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003ENoisy matrix completion aims at estimating a low-rank matrix given only partial and corrupted entries. Despite substantial progress in designing efficient estimation algorithms, it remains largely unclear how to assess the uncertainty of the obtained estimates and how to perform statistical inference on the unknown matrix (e.g. constructing a valid and short confidence interval for an unseen entry).\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThis talk takes a step towards inference and uncertainty quantification for noisy matrix completion. We develop a simple procedure to compensate for the bias of the widely used convex and nonconvex estimators. The resulting de-biased estimators admit nearly precise non-asymptotic distributional characterizations, which in turn enable optimal construction of confidence intervals\/regions for, say, the missing entries and the low-rank factors. Our inferential procedures do not rely on sample splitting, thus avoiding unnecessary loss of data efficiency. As a byproduct, we obtain a sharp characterization of the estimation accuracy of our de-biased estimators, which, to the best of our knowledge, are the first tractable algorithms that provably achieve full statistical efficiency (including the preconstant). The analysis herein is built upon the intimate link between convex and nonconvex optimization.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThis is joint work with Cong Ma, Yuling Yan, Yuejie Chi, and Jianqing Fan.\u003Cbr \/\u003E\r\n\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EBio: Yuxin Chen is currently an assistant professor in the Department of Electrical Engineering at Princeton\u0026nbsp;University. Prior to joining Princeton, he was a postdoctoral scholar in the Department of Statistics at\u0026nbsp;Stanford University, and he completed his Ph.D. in Electrical Engineering at Stanford University. His research\u0026nbsp;interests include high-dimensional statistics, convex and nonconvex optimization, statistical learning, and\u0026nbsp;information theory. He received the 2019 AFOSR Young Investigator Award.\u003Cbr \/\u003E\r\n\u0026nbsp;\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThis is one of a series of talks that are given by Professor Chen. The full list of his talks is as follows:\u003Cbr \/\u003E\r\nWednesday, August 28, 2019; 11:00 am - 12:00 pm; Groseclose 402\u003Cbr \/\u003E\r\nThursday, August 29, 2019; 11:00 am - 12:00 pm; Groseclose 402\u003Cbr \/\u003E\r\nTuesday, September 3, 2019; 11:00 am - 12:00 pm; Main - Executive Education Room 228\u003Cbr \/\u003E\r\nWednesday, September 4, 2019; 11:00 am - 12:00 pm; Main - Executive Education Room 228\u003Cbr \/\u003E\r\nThursday, September 5, 2019; 11:00 am - 12:00 pm; Groseclose 402\u003C\/p\u003E\r\n\r\n\u003Cp\u003ECheck https:\/\/triad.gatech.edu\/events for more information.\u0026nbsp;\u003Cbr \/\u003E\r\nFor location information, please check https:\/\/isye.gatech.edu\/about\/maps-directions\/isye-building-complex\u003Cbr \/\u003E\r\n\u0026nbsp;\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"This is one of a series of talks that are given by Professor Chen."}],"uid":"34963","created_gmt":"2019-08-25 17:38:07","changed_gmt":"2019-09-04 19:31:48","author":"Xiaoming Huo","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2019-09-05T12:00:00-04:00","event_time_end":"2019-09-05T13:00:00-04:00","event_time_end_last":"2019-09-05T13:00:00-04:00","gmt_time_start":"2019-09-05 16:00:00","gmt_time_end":"2019-09-05 17:00:00","gmt_time_end_last":"2019-09-05 17:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"related_links":[{"url":"http:\/\/www.princeton.edu\/~yc5\/slides\/NoisyMC_Inference_slides_Gatech.pdf","title":"Talk Slides at Speaker\u0027s web site"}],"groups":[{"id":"602673","name":"TRIAD "}],"categories":[],"keywords":[{"id":"92811","name":"data science"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"174045","name":"Graduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}