{"654683":{"#nid":"654683","#data":{"type":"event","title":"ARC Colloquium: Ainesh Bakshi (CMU)","body":[{"value":"\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EAlgorithms \u0026amp; Randomness Center (ARC)\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EAinesh Bakshi (CMU)\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EMonday, January 31, 2022\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp align = \u0022center\u0022\u003E\u003Cstrong\u003EVirtual via BlueJeans - 11:00 am\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ETitle:\u0026nbsp; \u003C\/strong\u003EAnalytic Techniques for Robust Algorithm Design\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u0026nbsp; \u003C\/strong\u003EModern machine learning relies on algorithms that fit expressive models to large datasets. While such tasks are easy in low dimensions, real-world datasets are truly high-dimensional. Additionally, a prerequisite to deploying models in real-world systems is to ensure that their behavior degrades gracefully when the modeling assumptions no longer hold. Therefore, there is a growing need for\u0026nbsp;\u003Cem\u003Eefficient algorithms\u003C\/em\u003E\u0026nbsp;that fit reliable and robust models to data.\u003Cbr \/\u003E\r\n\u003Cbr \/\u003E\r\nIn this talk, I will provide an overview of designing such efficient and robust algorithms, with provable guarantees, for fundamental tasks in machine learning and statistics. In particular, I will describe two complementary themes arising in this area:\u0026nbsp;\u003Cem\u003Ehigh-dimensional robust statistics\u003C\/em\u003E\u0026nbsp;and\u0026nbsp;\u003Cem\u003Efast numerical linear algebra\u003C\/em\u003E. The first addresses how to fit expressive models to high-dimensional datasets in the presence of outliers and the second develops fast algorithmic primitives to reduce dimensionality and de-noise large datasets. I will focus on recent results on robustly\u0026nbsp;learning mixtures of arbitrary Gaussians and describe the new algorithmic ideas obtained along the way. Finally, I will make the case for analytic techniques, such as convex relaxations, being the natural choice for robust algorithm design.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E----------------------------------\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Ca href=\u0022http:\/\/aineshbakshi.com\/\u0022\u003ESpeaker\u0026#39;s Webpage\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cem\u003EVideos of recent talks are available at: \u003C\/em\u003E\u003Ca href=\u0022https:\/\/smartech.gatech.edu\/handle\/1853\/46836\u0022\u003E\u003Cem\u003Ehttps:\/\/smartech.gatech.edu\/handle\/1853\/46836\u003C\/em\u003E\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Ca href=\u0022https:\/\/mailman.cc.gatech.edu\/mailman\/listinfo\/arc-colloq\u0022\u003E\u003Cem\u003EClick here to subscribe to the seminar email list: arc-colloq@Klauscc.gatech.edu \u003C\/em\u003E\u003C\/a\u003E\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Analytic Techniques for Robust Algorithm Design - Virtual via BlueJeans at 11am"}],"uid":"27544","created_gmt":"2022-01-21 20:52:15","changed_gmt":"2022-01-24 20:43:02","author":"Francella Tonge","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2022-01-31T11:00:00-05:00","event_time_end":"2022-01-31T12:00:00-05:00","event_time_end_last":"2022-01-31T12:00:00-05:00","gmt_time_start":"2022-01-31 16:00:00","gmt_time_end":"2022-01-31 17:00:00","gmt_time_end_last":"2022-01-31 17:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"70263","name":"ARC"}],"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":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}