Statistical Learning Theory and Stochastic Optimization by Jean Picard download in ePub, pdf, iPad
We expect that attendees not previously familiar with these topics too will be able to follow along, and enjoy, appreciate and learn from the tutorial. Fast convergence requires large learning rates but this may induce numerical instability.
Bartlett, Alexander Rakhlin, and Ambuj Tewari. First, the standard Statistical Learning setups, e. This is not at all surprising.
Problem complexity and method efficiency in optimization. Slides Slides presented at the tutorial are here. We will not expect any background in Stochastic Optimization, nor will we assume any specific knowledge in Statistical Learning. Shai Shalev-Shwartz and Nathan Srebro. Leon Bottou and Olivier Bousquet.
We will also study techniques for analyzing and proving performance guarantees for learning methods. Robust stochastic approximation approach to stochastic programming. Such schedules have been known since the work of MacQueen on k-means clustering.
On the generalization ability of on-line learning algorithms. References Jacob Abernethy, Peter L. The tradeoffs of large scale learning.
In particular, in machine learning, the need to set a learning rate step size has been recognized as problematic. On accelerated proximal gradient methods for convex-concave optimization. Lower bounds for oracle models in Stochastic Optimization can also sometimes be translated to learning lower bounds.
Our emphasis will be on concept development and on obtaining a rigorous quantitative understanding of machine learning. Fixed point and Bregman iterative methods for matrix rank minimization.
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