{"668988":{"#nid":"668988","#data":{"type":"event","title":"ISYE Statistic Seminar - Sabyasachi Chatterjee","body":[{"value":"\u003Cdiv\u003E\r\n\u003Cdiv dir=\u0022ltr\u0022\u003E\r\n\u003Cdiv dir=\u0022ltr\u0022\u003E\r\n\u003Cdiv\u003E\r\n\u003Cdiv\u003E\r\n\u003Cdiv\u003E\r\n\u003Cdiv\u003E\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003ETITLE\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E:\u0026nbsp; Theory for Cross Validation in Nonparametric Regression\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cdiv\u003E\r\n\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\r\n\r\n\u003Cdiv\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EABSTRACT\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E:\u0026nbsp; We formulate a general cross validation framework for signal\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003Edenoising. The general framework is then applied to nonparametric\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003Eregression methods such as Trend Filtering and Dyadic CART. The\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003Eresulting cross validated versions are then shown to attain nearly the\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003Esame rates of convergence as are known for the optimally tuned\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003Eanalogues. There did not exist any previous theoretical analyses of\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003Ecross validated versions of Trend Filtering or Dyadic CART. Our general\u0026nbsp;framework is inspired by the ideas in\u0026nbsp;\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EChatterjee\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u0026nbsp;and Jafarov (2015) and\u0026nbsp;is potentially applicable to a wide range of estimation methods which\u0026nbsp;use tuning parameters.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/div\u003E\r\n\r\n\u003Cdiv\u003E\u0026nbsp;\u003C\/div\u003E\r\n\u003C\/div\u003E\r\n\u003C\/div\u003E\r\n\r\n\u003Cdiv\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EBIO\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E: I am an Assistant Professor (from 2017 onwards) in the Statistics Department at\u0026nbsp;\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EUniversity\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u0026nbsp;of Illinois at Urbana Champaign. Most of my research has been in Nonparametric Function Estimation\/ Statistical Signal Processing. I am also interested in\u0026nbsp;Machine Learning and Probability. I obtained my Phd in 2014 at Yale\u0026nbsp;\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EUniversity\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u0026nbsp;and then was a Kruskal Instructor at\u0026nbsp;\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EUniversity\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u0026nbsp;of Chicago till 2017\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/div\u003E\r\n\u003C\/div\u003E\r\n\u003C\/div\u003E\r\n\u003C\/div\u003E\r\n\u003C\/div\u003E\r\n\u003C\/div\u003E\r\n\u003C\/div\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cdiv\u003E\r\n\u003Cdiv dir=\u0022ltr\u0022\u003E\r\n\u003Cdiv dir=\u0022ltr\u0022\u003E\r\n\u003Cdiv\u003E\r\n\u003Cdiv\u003E\r\n\u003Cdiv\u003E\r\n\u003Cdiv\u003E\r\n\u003Cdiv\u003E\r\n\u003Cdiv\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EWe formulate a general cross validation framework for signal\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003Cspan\u003Edenoising. The general framework is then applied to nonparametric\u0026nbsp;\u003C\/span\u003E\u003Cspan\u003Eregression methods such as Trend Filtering and Dyadic CART. The\u0026nbsp;\u003C\/span\u003E\u003Cspan\u003Eresulting cross validated versions are then shown to attain nearly the\u0026nbsp;\u003C\/span\u003E\u003Cspan\u003Esame rates of convergence as are known for the optimally tuned\u0026nbsp;\u003C\/span\u003E\u003Cspan\u003Eanalogues. There did not exist any previous theoretical analyses of\u0026nbsp;\u003C\/span\u003Ecross validated versions of Trend Filtering or Dyadic CART. Our general\u0026nbsp;framework is inspired by the ideas in Chatterjee and Jafarov (2015) and\u0026nbsp;is potentially applicable to a wide range of estimation methods which\u0026nbsp;use tuning parameters.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/div\u003E\r\n\u003C\/div\u003E\r\n\u003C\/div\u003E\r\n\u003C\/div\u003E\r\n\u003C\/div\u003E\r\n\u003C\/div\u003E\r\n\u003C\/div\u003E\r\n\u003C\/div\u003E\r\n\u003C\/div\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Theory for Cross Validation in Nonparametric Regression"}],"uid":"36433","created_gmt":"2023-08-16 12:30:44","changed_gmt":"2023-08-16 12:35:13","author":"mrussell89","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2023-08-29T13:00:00-04:00","event_time_end":"2023-08-29T14:00:00-04:00","event_time_end_last":"2023-08-29T14:00:00-04:00","gmt_time_start":"2023-08-29 17:00:00","gmt_time_end":"2023-08-29 18:00:00","gmt_time_end_last":"2023-08-29 18:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"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":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}