{"id":49031,"date":"2024-08-26T10:28:11","date_gmt":"2024-08-26T14:28:11","guid":{"rendered":"https:\/\/engineering.jhu.edu\/ams\/?post_type=tribe_events&p=49031"},"modified":"2024-10-23T09:41:53","modified_gmt":"2024-10-23T13:41:53","slug":"ams-weekly-seminar-professors-laurent-younes-don-geman","status":"publish","type":"tribe_events","link":"https:\/\/engineering.jhu.edu\/ams\/event\/ams-weekly-seminar-professors-laurent-younes-don-geman\/","title":{"rendered":"AMS Weekly Seminar | Laurent Younes & Don Geman"},"content":{"rendered":"

Location:\u00a0<\/strong>Gilman 50<\/p>\n

When:<\/strong>\u00a0October 24th at 1:30 p.m.<\/p>\n

Title:\u00a0<\/strong>Feature Selection and Gene Panels<\/span><\/p>\n

Abstract:\u00a0<\/strong>Simplicity and explainability of statistical decision and prediction have become increasingly relevant in the past few years, especially in the biological and medical fields. In this context, being able to focus on a small number of selected variables is an essential requirement and we will discuss some of the methods we recently developed for this purpose, with applications in computational biology. We will introduce, in particular, a family of algorithms that formulate feature selection as a set covering problem, with several variations on how the covering sets are defined. We will also describe an information theoretic approach, called \u201cinformation pursuit\u201d that progressively builds a feature panel based on a stepwise maximization of mutual information.<\/span><\/p>\n

Zoom link:\u00a0<\/strong>https:\/\/wse.zoom.us\/j\/96299385386?pwd=HvOzDnw7A2SOQkMrbBbr5AXjqurdj0.1<\/p>\n","protected":false},"excerpt":{"rendered":"

Location:\u00a0Gilman 50 When:\u00a0October 24th at 1:30 p.m. Title:\u00a0Feature Selection and Gene Panels Abstract:\u00a0Simplicity and explainability of statistical decision and prediction have become increasingly relevant in the past few years, 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