• Click on the titles of individual talks for abstract, slides and archived video.

The rump session on Thursday evening will be held in the Whitecotton Room at the Hotel Shattuck Plaza. All other events will take place in the Calvin Lab Auditorium.

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Coffee and Check-In
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Opening Remarks
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Selective Inference and False Discovery Rate I

Yoav Benjamini, Tel Aviv University
Video
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Break
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Selective Inference and False Discovery Rate II

Yoav Benjamini, Tel Aviv University
Video
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Musings on False Discovery Rate

Omer Reingold, Microsoft Research
Video
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Lunch Break
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Tutorial on Differential Privacy

Katrina Ligett, California Institute of Technology
Video
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Break
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DP-WHERE: Differentially Private Modeling of Human Mobility

Rebecca Wright, Rutgers University
Video
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Real-time Aggregate Monitoring with Differential Privacy

Li Xiong, Emory University
Video
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Dual Query Release: A Practical Algorithm for Private Analysis of High Dimensional Data

Justin Hsu, University of Pennsylvania
Video
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Differential Privacy and Stochastic Gradient Descent

Anand Sarwate, Toyota Technological Institute, Chicago
Video
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Reception
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Coffee and Check-In
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Learning and DP

Jonathan Ullman, Harvard University
Video
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Break
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Active Statistical Query Learning with Connections to Privacy

Nina Balcan, Georgia Institute of Technology
Video
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Lunch Break
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From Stability to Differential Privacy

Abhradeep Guha Thakurta, Stanford University and Microsoft Research
Video
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Break
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A Stability-based Validation Procedure for Differentially Private Machine Learning

Kamalika Chaudhuri (UC San Diego)
Video
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Privacy, Forward Stability and Online Learning

Adam Smith, Pennsylvania State University
Video
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Extremal Mechanisms for Differential Privacy

Pramod Viswanath, University of Illinois, Urbana-Champaign
Video
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Dinner Break
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Rump Session I (Hotel Shattuck Plaza)
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Break
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Rump Session II (Hotel Shattuck Plaza)
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Coffee and Check-In
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Local Privacy and Minimax Rates

John Duchi (Stanford University)
Video
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Break
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Sparse PCA in High Dimensions

Jing Lei, Carnegie Mellon University
Video
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Private Learning and Sanitization: Pure vs. Approximate Differential Privacy

Kobbi Nissim, Ben-Gurion University of the Negev
Video
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Lunch Break
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DP in the Streaming Model

Sasho Nikolov, Rutgers University
Video
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Turning Privacy-Utility Trade-offs Using Policies and an Application to US Census

Ashwin Machanavajjhala, Duke University
Video
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Break
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Differentially Private Empirical Risk Minimization

Prateek Jain, Microsoft Research India
Video
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Break
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The Power of Linear Reconstruction Attacks

Shiva Kasiviswanathan, GE Global Research
Video
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Coffee and Check-In
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DP for Graphs and Social Networks

Sofya Raskhodnikova, Pennsylvania State University
Video
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Break
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Geometry of DP

Li Zhang, Microsoft Research
Video
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Lunch Break
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From Matrix Completion to Private Singular Vector Computation and Back

Moritz Hardt (UC Berkeley)
Video
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Randomized Response for Private Principal Component Analysis

Kunal Talwar (Apple Inc)
Video
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Break
Wrap-up Discussion