Playlist: 25 videos

Computational Challenges in Machine Learning

The aim of this workshop is to bring together a broad set of researchers looking at algorithmic questions that arise in machine learning. The primary target areas will be large-­scale learning, including algorithms for Bayesian estimation and...

0:45:18

Efficient Distributed Deep Learning Using MXNet

Anima Anandkumar, UC Irvine
Computational Challenges in Machine Learning
https://simons.berkeley.edu/talks/anima-anandkumar-2017-5-5
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1:9:46

Machine Learning for Healthcare Data

Katherine Heller, Duke University
Computational Challenges in Machine Learning
https://simons.berkeley.edu/talks/katherine-heller-2017-5-3
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0:50:12

Machine Learning Combinatorial Optimization Algorithms

Dorit Hochbaum, UC Berkeley
Computational Challenges in Machine Learning
https://simons.berkeley.edu/talks/dorit-hochbaum-2017-5-3
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0:40:49

A Cost Function for Similarity-Based Hierarchical Clustering

Sanjoy Dasgupta, UC San Diego
Computational Challenges in Machine Learning
https://simons.berkeley.edu/talks/tba-3
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0:45:27

The Imitation Learning View of Structured Prediction

Hal Daume, University of Maryland at College Park
Computational Challenges in Machine Learning
https://simons.berkeley.edu/talks/hal-daume-2017-5-3
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0:29:39

Exponential Computational Improvement by Reduction

John Langford, Microsoft Research New York
Computational Challenges in Machine Learning
https://simons.berkeley.edu/talks/john-langford-2017-5-3
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1:8:2

Embedding as a Tool for Algorithm Design

Le Song, Georgia Institute of Technology
Computational Challenges in Machine Learning
https://simons.berkeley.edu/talks/le-song-2017-5-4
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0:35:35

Robust Estimation of Mean and Covariance

Anup Rao, Georgia Institute of Technology
Computational Challenges in Machine Learning
https://simons.berkeley.edu/talks/anup-rao-2017-5-4
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0:49:33

Computational Efficiency and Robust Statistics

Ilias Diakonikolas, University of Southern California
Computational Challenges in Machine Learning
https://simons.berkeley.edu/talks/ilias-daikonikolas-2017-5-4
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0:42:11

System and Algorithm Co-Design, Theory and Practice, for Distributed Machine Learning

Eric Xing, Carnegie Mellon University
Computational Challenges in Machine Learning
https://simons.berkeley.edu/talks/tba-4
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