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

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Coffee and Check-In
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Opening Remarks
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Foundations of Data Science I
Ravi Kannan (Simons Institute, UC Berkeley)
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Break
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Foundations of Data Science II
Ravi Kannan (Simons Institute, UC Berkeley)
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Lunch
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Sketching for Linear Algebra: Basics of Dimensionality Reduction and CountSketch I
David Woodruff (Carnegie Mellon University)
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Break
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Sketching for Linear Algebra: Basics of Dimensionality Reduction and CountSketch II
David Woodruff (Carnegie Mellon University)
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Welcome Reception
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Coffee and Check-In
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Sketching for Linear Algebra III: Randomized Hadamard, Kernel Methods
Ken Clarkson (IBM Almaden)
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Break
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First-Order Stochastic Optimization
Rachel Ward, University of Texas at Austin
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Lunch
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Sampling for Linear Algebra, Statistics, and Optimization I
Michael Mahoney, International Computer Science Institute and UC Berkeley
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Break
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Sampling for Linear Algebra, Statistics, and Optimization II
Michael Mahoney, International Computer Science Institute and UC Berkeley
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Coffee and Check-In
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Stochastic Second Order Optimization Methods I
Fred Roosta, University of Queensland
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Break
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Stochastic Second Order Optimization Methods II
Fred Roosta, University of Queensland
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Lunch
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Statistical Inference I
Will Fithian, UC Berkeley
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Break
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Statistical Inference II
Will Fithian, UC Berkeley
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Coffee and Check-In
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High Dimensional Geometry and Concentration I
Santosh Vempala (Georgia Institute of Technology)
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Break
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High Dimensional Geometry and Concentration II
Santosh Vempala (Georgia Institute of Technology)
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Lunch
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Algorithmic High Dimensional Robust Statistics I
Ilias Diakonikolas (University of Wisconsin-Madison)
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Break
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Algorithmic High Dimensional Robust Statistics II
Ilias Diakonikolas (University of Wisconsin-Madison)
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Coffee and Check-In
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Nearest Neighbor Methods I
Ilya Razenshteyn, Microsoft Research
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Break
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Nearest Neighbor Methods II
Ilya Razenshteyn, Microsoft Research
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Lunch
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Data Streams I
Michael Kapralov, EPFL
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Break
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Data Streams II
Michael Kapralov, EPFL