{"568751":{"#nid":"568751","#data":{"type":"event","title":"ARC10","body":[{"value":"\u003Ch4 align=\u0022center\u0022\u003E\u003Cstrong\u003EARC10\u003C\/strong\u003E\u003C\/h4\u003E\r\n\r\n\u003Ch5 align=\u0022center\u0022\u003E\u003Cstrong\u003EA Special Event Celebrating the 10\u003Csup\u003Eth\u003C\/sup\u003E Anniversary of Georgia Tech\u0026#39;s\u003C\/strong\u003E\u003C\/h5\u003E\r\n\r\n\u003Ch5 align=\u0022center\u0022\u003E\u003Cstrong\u003EAlgorithms and Randomness Center\u003C\/strong\u003E\u003C\/h5\u003E\r\n\r\n\u003Ch5 align=\u0022center\u0022\u003E\u0026nbsp;\u003Cstrong\u003EMonday, October 24 in Klaus 1116\u003C\/strong\u003E\u003C\/h5\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EProgram Schedule:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E9:30\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Refreshments\u003C\/p\u003E\r\n\r\n\u003Cp\u003E10:00am: \u0026nbsp;\u003Cstrong\u003E ARC-IDEaS Distinguished Lecture by Jon Kleinberg\u003C\/strong\u003E \u0026ndash; Cornell University\u003Cbr \/\u003E\r\n\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003Ca href=\u0022http:\/\/arc.gatech.edu\/hg\/item\/560251\u0022\u003ETitle: \u003Cem\u003EHuman Decisions and Machine Predictions\u003C\/em\u003E\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E11:00am: \u0026nbsp; Break\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E11:15am: \u0026nbsp; Josephine Yu - GT Math\u0026nbsp;\u003Cbr \/\u003E\r\n\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Title:\u0026nbsp;\u003Cem\u003ETropical Geometry in Economics\u003C\/em\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E11:50am: \u0026nbsp; Mohit Singh - Microsoft\/GT ISyE\u0026nbsp;\u003Cbr \/\u003E\r\n\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Title:\u0026nbsp;\u003Cem\u003ENew Approaches for Constrained Subset Selection Problem\u003C\/em\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;12:30\u0026nbsp;\u0026nbsp; Lunch in the Klaus Atrium\u003C\/p\u003E\r\n\r\n\u003Cp\u003E(Sponsored by Georgia Tech Colleges of Computing, Engineering, and\u0026nbsp;Sciences, and by Microsoft Research.)\u003C\/p\u003E\r\n\r\n\u003Cp align=\u0022center\u0022\u003E* * * * *\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ESpeaker Abstracts:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EJon Kleinberg\u003C\/strong\u003E\u003Cstrong\u003E\u0026nbsp;\u003C\/strong\u003E\u0026ndash; Cornell University\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E \u0026nbsp;\u003Cbr \/\u003E\r\n\u003Cem\u003EHuman Decisions and Machine Predictions\u003C\/em\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003Cbr \/\u003E\r\nAn increasing number of domains are providing us with detailed trace data on human decisions, often made by experts with deep experience in the subject matter. This provides an opportunity to use machine-learning prediction algorithms to ask several families of questions --- not only about the extent to which algorithms can outperform expert-level human decision-making in specific domains, but also whether we can use algorithms to analyze the nature of the errors made by human experts, to predict which instances will be hardest for these experts, and to explore some of the ways in which prediction algorithms can serve as supplements to human decision-making in different applications.\u0026nbsp; In this talk, I\u0026#39;ll explore this theme by drawing on a line of recent projects; all are joint with Sendhil Mullainathan, and include collaborations with Ashton Anderson, Himabindu Lakkaraju, Jure Leskovec, Annie Liang, and Jens Ludwig.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003Cbr \/\u003E\r\nJon Kleinberg is the Tisch University Professor in the Departments of Computer Science and Information Science at Cornell University. His research focuses on issues at the interface of networks and information, with an emphasis on the social and information networks that underpin the Web and other on-line media. He is a member of the National Academy of Sciences, the National Academy of Engineering, and the American Academy of Arts and Science; and he is the recipient of research fellowships from the MacArthur, Packard, Simons, and Sloan Foundations, as well as awards including the Nevanlinna Prize, the Harvey Prize, the Newell Award, and the ACM-Infosys Foundation Award in the Computing Sciences.\u003C\/p\u003E\r\n\r\n\u003Cp align=\u0022center\u0022\u003E* * * * *\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EJosephine Yu\u003C\/strong\u003E\u003Cstrong\u003E\u0026nbsp;\u003C\/strong\u003E\u0026ndash; Georgia Tech Math\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ETitle: \u003C\/strong\u003E\u003Cbr \/\u003E\r\n\u003Cem\u003ETropical Geometry in Economics\u003C\/em\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract: \u003C\/strong\u003E\u003Cbr \/\u003E\r\nIn a recent and ongoing work, Baldwin and Klemperer explored a connection between tropical geometry and economics. They gave a sufficient condition for the existence of competitive equilibrium in product-mix auctions of indivisible goods. This result, which we call the Unimodularity Theorem, can also be traced back to the work of Danilov, Koshevoy, and Murota. We will introduce product-mix\u0026nbsp;auctions, prove the Unimodularity Theorem, and discuss integer programming formulations of this problem.\u0026nbsp; This is based on a joint work with Ngoc Mai Tran.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EBio: \u003C\/strong\u003E\u003Cbr \/\u003E\r\nJosephine Yu completed her Ph.D. in mathematics at UC Berkeley in 2007.\u0026nbsp; After postdoctoral positions at MIT and the Mathematical Sciences Research Institute in Berkeley, she joined the School of Mathematics at Georgia Tech in 2010.\u0026nbsp; Her research spans combinatorics, computational algebra, and polyhedral and algebraic geometry.\u003C\/p\u003E\r\n\r\n\u003Cp align=\u0022center\u0022\u003E* * * * *\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003Cstrong\u003EMohit Singh\u003C\/strong\u003E\u003Cstrong\u003E\u0026nbsp;\u003C\/strong\u003E\u0026ndash; Microsoft \/ Georgia Tech ISyE\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ETitle: \u0026nbsp;\u0026nbsp;\u003C\/strong\u003E\u003Cbr \/\u003E\r\n\u003Cem\u003ENew Approaches for Constrained Subset Selection Problem\u003C\/em\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract: \u003C\/strong\u003E\u003Cbr \/\u003E\r\nSelecting a diverse subset of items from a collection occurs as a fundamental problem in various applications including selecting representative documents from a corpus, selecting diverse geographical locations for sensor placement and designing most informative experiments. The problem is modeled by maximizing the entropy of the joint distribution of the selected items and, typically, has additional side constraints. We design approximation algorithms for the subset selection problem with additional partition constraints. Our algorithm relies on efficient polynomial optimization and reveals surprising connections to the problem of counting matchings via the theory of hyperbolic polynomials.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003Cbr \/\u003E\r\nMohit Singh is a researcher in the theory group at Microsoft Research, Redmond and will be joining ISyE, Georgia Tech in January 2017. His research interests include discrete optimization, approximation algorithms and convex optimization. Previously, he was an Assistant Professor at McGill University from 2010-2011 and a post-doctoral researcher at Microsoft Research, New England from 2008-2009. He obtained his Ph.D. from Tepper School of Business, Carnegie Mellon University and his doctoral thesis received the Tucker prize in 2009 given by the Mathematical Optimization Society. He has also received the best paper award for his work on the traveling salesman problem at the Annual Symposium on Foundations of Computer Science (FOCS) 2011.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"10th anniversary celebration of the Algorithms \u0026 Randomness Center featuring Jon Kleinberg (Cornell)"}],"uid":"27466","created_gmt":"2016-08-26 12:53:58","changed_gmt":"2017-04-13 21:14:57","author":"Dani Denton","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2016-10-24T10:30:00-04:00","event_time_end":"2016-10-24T14:30:00-04:00","event_time_end_last":"2016-10-24T14:30:00-04:00","gmt_time_start":"2016-10-24 14:30:00","gmt_time_end":"2016-10-24 18:30:00","gmt_time_end_last":"2016-10-24 18:30:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"70263","name":"ARC"},{"id":"47223","name":"College of Computing"},{"id":"50875","name":"School of Computer Science"}],"categories":[],"keywords":[{"id":"111051","name":"Algorithm and Randomness Center"},{"id":"4265","name":"ARC"},{"id":"168769","name":"ARC 10"},{"id":"115001","name":"Computational Complexity"},{"id":"114991","name":"Computational Learning Theory"},{"id":"109","name":"Georgia Tech"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"78751","name":"Undergraduate students"},{"id":"174045","name":"Graduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EDani Denton\u003Cbr \/\u003E\r\ndenton at cc dot gatech dot edu\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}