{"55533":{"#nid":"55533","#data":{"type":"event","title":"MLDM Seminar: John Lafferty","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EJohn Lafferty\u003C\/strong\u003E\u003Cbr \/\u003ESchool of Computer Science\u003Cbr \/\u003ECarnegie Mellon University\u003Cstrong\u003E\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003E\u0022Nonparametric Graphical Models\u0022\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EGraphical modeling has proven to be an extremely useful abstraction in statistical machine learning.\u0026nbsp; The space of possible graphical models is enormous, yet only a very limited set of models has been extensively developed for continuous data.\u0026nbsp; The most basic, classical example is the Gaussian graphical model, where the precision matrix encodes the independence graph.\u0026nbsp; While Gaussian graphical models can be useful, a reliance on exact normality is limiting.\u0026nbsp; We present recent work for estimating nonparametric graphical models.\u0026nbsp; One approach is something we call \u0022the nonparanormal,\u0022 which uses copula methods to transform the variables by nonparametric functions, relaxing the strong distributional assumptions made by the Gaussian graphical model.\u0026nbsp; Another approach is to restrict the family of allowed graphs to spanning forests, enabling the use of fully nonparametric density estimation.\u0026nbsp; The resulting methods are easy to understand, simple to use, theoretically well supported, and effective for modeling of high dimensional data.\u0026nbsp; Joint work with Anupam Gupta, Han Liu, Larry Wasserman, and Min Xu.\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EJohn Lafferty is a professor in the Computer Science Department and the Machine Learning Department within the School of Computer Science at Carnegie Mellon University, where he also holds a joint appointment in the Department of Statistics.\u0026nbsp; His research interests are in text analysis, machine learning, and statistical learning theory, with a recent focus on theory and methods for high dimensional data.\u003C\/p\u003E\u003Cp\u003ECourtesy of our generous sponsor, Yahoo!\u003C\/p\u003E\u003Cp\u003EFree Pizza!\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Nonparametric Graphical Models"}],"uid":"27154","created_gmt":"2010-04-30 17:43:09","changed_gmt":"2016-10-08 01:51:21","author":"Louise Russo","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2010-05-06T13:00:00-04:00","event_time_end":"2010-05-06T14:00:00-04:00","event_time_end_last":"2010-05-06T14:00:00-04:00","gmt_time_start":"2010-05-06 17:00:00","gmt_time_end":"2010-05-06 18:00:00","gmt_time_end_last":"2010-05-06 18:00:00","rrule":null,"timezone":"America\/New_York"},"extras":["free_food"],"groups":[{"id":"37041","name":"Computational Science and Engineering"},{"id":"47223","name":"College of Computing"},{"id":"50877","name":"School of Computational Science and Engineering"}],"categories":[],"keywords":[{"id":"9231","name":"MLDM"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Ca href=\u0022http:\/\/www.cse.gatech.edu\/people\/alexander-gray\u0022 target=\u0022_self\u0022\u003EAlex Gray\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}