{"590495":{"#nid":"590495","#data":{"type":"event","title":"ML@GT Seminar by Yao Xie","body":[{"value":"\u003Cp\u003EAbstract: Change-point detection is a classic statistical framework for detecting a change in the distribution\u0026nbsp;of a sequence of data. In this talk, I will focus on its connection with machine learning and anomaly\u0026nbsp;detection, and illustrate by our two recent work along this direction. While classic change-point detection\u0026nbsp;usually assumes i.i.d. data and parametric forms of the data distributions, when dealing with machine\u0026nbsp;learning problems we may need to go beyond these settings. The first work considers detecting a change in a\u0026nbsp;network where one observes a sequence of correlated discrete events on the nodes. The second work\u0026nbsp;presents a distribution-free kernel based method leveraging minimum mean discrepancy (MMD) statistic.\u0026nbsp;The common themes are to construct detection statistics that are suitable for machine learning tasks and to\u0026nbsp;control the false alarm rate via a powerful change-of-measure technique. This is a joint work with Shuang Li,\u0026nbsp;Le\u0026nbsp;Song, Mehrdad Farajtba and Apart Verma.\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\nBio: Yao Xie is an Assistant Professor in the H. Milton Stewart School of Industrial and Systems\u0026nbsp;Engineering, Georgia Institute of Technology. She received her Ph.D. in Electrical Engineering (minor in\u0026nbsp;Mathematics) from Stanford University in 2011. Prior joining Georgia Tech, she worked as a Research\u0026nbsp;Scientist at Duke University. Her research areas include computational statistics, signal processing, and\u0026nbsp;machine learning, in providing theoretical insights, developing computationally efficient and statistically\u0026nbsp;powerful algorithms for various application, including sensor networks, social networks, imaging, material\u0026nbsp;science, geophysics, communications. She received a Best Student Paper Award at Annual Asilomar\u0026nbsp;Conference on Signals, Systems and Computers in 2005, Finalist of Best Student Paper Award in ICASSP\u0026nbsp;Conference in 2007, and the National Science Foundation (NSF) CAREER Award in 2017.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Change-point detection meets machine learning"}],"uid":"34417","created_gmt":"2017-04-17 13:29:19","changed_gmt":"2017-04-17 13:30:43","author":"jkwon47","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2017-03-15T13:00:00-04:00","event_time_end":"2017-03-15T14:00:00-04:00","event_time_end_last":"2017-03-15T14:00:00-04:00","gmt_time_start":"2017-03-15 17:00:00","gmt_time_end":"2017-03-15 18:00:00","gmt_time_end_last":"2017-03-15 18:00:00","rrule":null,"timezone":"America\/New_York"},"extras":["free_food"],"groups":[{"id":"576481","name":"ML@GT"}],"categories":[],"keywords":[{"id":"173894","name":"ML@GT"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}