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

 All events 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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Optimization for Machine Learning I
Elad Hazan, Princeton University
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Break
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Optimization for Machine Learning II
Elad Hazan, Princeton University
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Lunch
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Submodularity: Theory and Applications I
Andreas Krause, ETH Zürich, Stefanie Jegelka, MIT
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Break
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Submodularity: Theory and Applications II
Andreas Krause, ETH Zürich, Stefanie Jegelka, MIT
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Coffee and Check-In
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A Tutorial on Reinforcement Learning I
Emma Brunskill, Carnegie Mellon University
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Break
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A Tutorial on Reinforcement Learning II
Emma Brunskill, Carnegie Mellon University
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Lunch
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Interactive Learning of Classifiers and Other Structures
Sanjoy Dasgupta, UC San Diego, Rob Nowak, University of Wisconsin-Madison
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Break
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Deep Robotic Learning
Sergey Levine, UC Berkeley
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Coffee and Check-In
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Nonparametric Bayesian Methods: Models, Algorithms, and Applications I
Tamara Broderick, MIT, Michael Jordan, UC Berkeley
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Break
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Nonparametric Bayesian Methods: Models, Algorithms, and Applications II
Tamara Broderick, MIT, Michael Jordan, UC Berkeley
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Lunch
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Nonparametric Bayesian Methods: Models, Algorithms, and Applications III
Tamara Broderick, MIT, Michael Jordan, UC Berkeley
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Break
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Nonparametric Bayesian Methods: Models, Algorithms, and Applications IV
Tamara Broderick, MIT, Michael Jordan, UC Berkeley
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Coffee and Check-In
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Tutorial on Deep Learning I
Ruslan Salakhutdinov, Carnegie Mellon University
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Break
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Tutorial on Deep Learning II
Ruslan Salakhutdinov, Carnegie Mellon University
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Lunch
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Tutorial on Deep Learning III
Ruslan Salakhutdinov, Carnegie Mellon University
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Break
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Tutorial on Deep Learning IV
Ruslan Salakhutdinov, Carnegie Mellon University
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Coffee and Check-In
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Tensor Decompositions for Learning Latent Variable Models I
Daniel Hsu, Columbia University
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Break
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Tensor Decompositions for Learning Latent Variable Models II
Daniel Hsu, Columbia University
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Lunch
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Natural Language Understanding: Foundations and State-of-the-Art
Percy Liang, Stanford University