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Oral
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Wed 3:00
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Stochastic Online AUC Maximization
Yiming Ying · Longyin Wen · Siwei Lyu
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Poster
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Wed 9:00
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Stochastic Online AUC Maximization
Yiming Ying · Longyin Wen · Siwei Lyu
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Poster
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Tue 9:00
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Stochastic Variance Reduction Methods for Saddle-Point Problems
Balamurugan Palaniappan · Francis Bach
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Poster
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Tue 9:00
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One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities
Michalis Titsias
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Workshop
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Sat 2:00
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Jeff Dean – TensorFlow: Future Directions for Simplifying Large-Scale Machine Learning
Jeff Dean
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Workshop
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Fri 8:00
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Invited Talk: Learning Adaptive Driving Models from Large-scale Video Datasets (Fisher Yu, Huazhe Xu, Dequan Wang, and Trevor Darrell, Berkeley)
Trevor Darrell
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Poster
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Wed 9:00
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The Robustness of Estimator Composition
Pingfan Tang · Jeff M Phillips
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Poster
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Wed 9:00
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Generating Videos with Scene Dynamics
Carl Vondrick · Hamed Pirsiavash · Antonio Torralba
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Poster
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Mon 9:00
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Greedy Feature Construction
Dino Oglic · Thomas Gärtner
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Poster
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Mon 9:00
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Learning the Number of Neurons in Deep Networks
Jose M. Alvarez · Mathieu Salzmann
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Poster
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Mon 9:00
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Bootstrap Model Aggregation for Distributed Statistical Learning
JUN HAN · Qiang Liu
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Poster
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Tue 9:00
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Stochastic Gradient Geodesic MCMC Methods
Chang Liu · Jun Zhu · Yang Song
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