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Author Information
Christina Lee (Microsoft Research)
Asuman Ozdaglar (Massachusetts Institute of Technology)
Asu Ozdaglar received the B.S. degree in electrical engineering from the Middle East Technical University, Ankara, Turkey, in 1996, and the S.M. and the Ph.D. degrees in electrical engineering and computer science from the Massachusetts Institute of Technology, Cambridge, in 1998 and 2003, respectively. She is currently a professor in the Electrical Engineering and Computer Science Department at the Massachusetts Institute of Technology. She is also the director of the Laboratory for Information and Decision Systems. Her research expertise includes optimization theory, with emphasis on nonlinear programming and convex analysis, game theory, with applications in communication, social, and economic networks, distributed optimization and control, and network analysis with special emphasis on contagious processes, systemic risk and dynamic control. Professor Ozdaglar is the recipient of a Microsoft fellowship, the MIT Graduate Student Council Teaching award, the NSF Career award, the 2008 Donald P. Eckman award of the American Automatic Control Council, the Class of 1943 Career Development Chair, the inaugural Steven and Renee Innovation Fellowship, and the 2014 Spira teaching award. She served on the Board of Governors of the Control System Society in 2010 and was an associate editor for IEEE Transactions on Automatic Control. She is currently the area coeditor for a new area for the journal Operations Research, entitled "Games, Information and Networks. She is the coauthor of the book entitled âConvex Analysis and Optimizationâ (Athena Scientific, 2003).
Devavrat Shah (Massachusetts Institute of Technology)
Devavrat Shah is a professor of Electrical Engineering & Computer Science and Director of Statistics and Data Science at MIT. He received PhD in Computer Science from Stanford. He received Erlang Prize from Applied Probability Society of INFORMS in 2010 and NeuIPS best paper award in 2008.
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2020 Poster: Personalized Federated Learning with Theoretical Guarantees: A ModelAgnostic MetaLearning Approach »
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2019 Poster: On Robustness of Principal Component Regression »
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2019 Oral: On Robustness of Principal Component Regression »
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2019 Poster: A Universally Optimal Multistage Accelerated Stochastic Gradient Method »
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2018 Poster: Qlearning with Nearest Neighbors »
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2018 Poster: Escaping Saddle Points in Constrained Optimization »
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2017 Workshop: Nearest Neighbors for Modern Applications with Massive Data: An Ageold Solution with New Challenges »
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2017 Poster: Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation »
Christian Borgs · Jennifer Chayes · Christina Lee · Devavrat Shah 
2017 Poster: When Cyclic Coordinate Descent Outperforms Randomized Coordinate Descent »
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2016 Poster: Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering »
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2015 Invited Talk: Incremental Methods for Additive Cost Convex Optimization »
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2014 Workshop: Analysis of Rank Data: Confluence of Social Choice, Operations Research, and Machine Learning »
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2014 Poster: Hardness of parameter estimation in graphical models »
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2014 Poster: A Latent Source Model for Online Collaborative Filtering »
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2014 Poster: Learning Mixed Multinomial Logit Model from Ordinal Data »
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2014 Poster: Structure learning of antiferromagnetic Ising models »
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2013 Workshop: Crowdsourcing: Theory, Algorithms and Applications »
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2013 Poster: A Latent Source Model for Nonparametric Time Series Classification »
George H Chen · Stanislav Nikolov · Devavrat Shah 
2012 Poster: Iterative ranking from pairwise comparisons »
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2011 Poster: Iterative Learning for Reliable Crowdsourcing Systems »
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2011 Oral: Iterative Learning for Reliable Crowdsourcing Systems »
David R Karger · Sewoong Oh · Devavrat Shah 
2009 Poster: A DataDriven Approach to Modeling Choice »
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2008 Poster: Inferring rankings under constrained sensing »
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2007 Spotlight: Message Passing for Maxweight Independent Set »
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