Workshops

Spring 2017

Foundations of Machine Learning Boot Camp

Jan. 23 – Jan. 27, 2017

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

All events take place in the Calvin Lab Auditorium.

Monday, January 23rd, 2017
9:00 am – 9:20 am		Coffee and Check-In
9:20 am – 9:30 am		Opening Remarks
9:30 am – 10:30 am		Optimization for Machine Learning I Elad Hazan, Princeton University
10:30 am – 11:00 am		Break
11:00 am – 12:00 pm		Optimization for Machine Learning II Elad Hazan, Princeton University
12:00 pm – 2:00 pm		Lunch
2:00 pm – 3:00 pm		Submodularity: Theory and Applications I Andreas Krause, ETH Zürich and Stefanie Jegelka, MIT
3:00 pm – 3:30 pm		Break
3:30 pm – 4:30 pm		Submodularity: Theory and Applications II Andreas Krause, ETH Zürich and Stefanie Jegelka, MIT

Tuesday, January 24th, 2017
9:00 am – 9:30 am		Coffee and Check-In
9:30 am – 10:30 am		A Tutorial on Reinforcement Learning I Emma Brunskill, Carnegie Mellon University
10:30 am – 11:00 am		Break
11:00 am – 12:00 pm		A Tutorial on Reinforcement Learning II Emma Brunskill, Carnegie Mellon University
12:00 pm – 1:30 pm		Lunch
1:30 pm – 3:00 pm		Interactive Learning of Classifiers and Other Structures Sanjoy Dasgupta, UC San Diego and Rob Nowak, University of Wisconsin-Madison
3:00 pm – 3:30 pm		Break
3:30 pm – 5:00 pm		Deep Robotic Learning Sergey Levine, UC Berkeley

Wednesday, January 25th, 2017
9:00 am – 9:30 am		Coffee and Check-In
9:30 am – 10:30 am		Nonparametric Bayesian Methods: Models, Algorithms, and Applications I Tamara Broderick, MIT and Michael Jordan, UC Berkeley
10:30 am – 11:00 am		Break
11:00 am – 12:00 pm		Nonparametric Bayesian Methods: Models, Algorithms, and Applications II Tamara Broderick, MIT and Michael Jordan, UC Berkeley
12:00 pm – 2:00 pm		Lunch
2:00 pm – 3:00 pm		Nonparametric Bayesian Methods: Models, Algorithms, and Applications III Tamara Broderick, MIT and Michael Jordan, UC Berkeley
3:00 pm – 3:30 pm		Break
3:30 pm – 4:30 pm		Nonparametric Bayesian Methods: Models, Algorithms, and Applications IV Tamara Broderick, MIT and Michael Jordan, UC Berkeley

Thursday, January 26th, 2017
9:00 am – 9:30 am		Coffee and Check-In
9:30 am – 10:30 am		Tutorial on Deep Learning I Ruslan Salakhutdinov, Carnegie Mellon University
10:30 am – 11:00 am		Break
11:00 am – 12:00 pm		Tutorial on Deep Learning II Ruslan Salakhutdinov, Carnegie Mellon University
12:00 pm – 2:00 pm		Lunch
2:00 pm – 3:00 pm		Tutorial on Deep Learning III Ruslan Salakhutdinov, Carnegie Mellon University
3:00 pm – 3:30 pm		Break
3:30 pm – 4:30 pm		Tutorial on Deep Learning IV Ruslan Salakhutdinov, Carnegie Mellon University

Friday, January 27th, 2017
9:00 am – 9:30 am		Coffee and Check-In
9:30 am – 10:30 am		Tensor Decompositions for Learning Latent Variable Models I Daniel Hsu, Columbia University
10:30 am – 11:00 am		Break
11:00 am – 12:00 pm		Tensor Decompositions for Learning Latent Variable Models II Daniel Hsu, Columbia University
12:00 pm – 2:00 pm		Lunch
2:00 pm – 3:30 pm		Natural Language Understanding: Foundations and State-of-the-Art Percy Liang, Stanford University