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Perturbations, Optimization, and Statistics
Tamir Hazan · George Papandreou · Danny Tarlow

Fri Dec 12 05:30 AM -- 03:30 PM (PST) @ Level 5; room 515 b,c
Event URL: http://www.cs.toronto.edu/~dtarlow/pos14 »

In nearly all machine learning tasks, decisions must be made given current knowledge (e.g., choose which label to predict). Perhaps surprisingly, always making the best decision is not always the best strategy, particularly while learning. Recently, there is an emerging body of work on learning under different rules that apply perturbations to the decision procedure. These works provide simple and efficient learning rules with improved theoretical guarantees. This workshop will bring together the growing community of researchers interested in different aspects of this area, and it will broaden our understanding of why and how perturbation methods can be useful.

In the last couple of years, at the highly successful NIPS workshops on Perturbations, Optimization, and Statistics, we looked at how injecting perturbations (whether it be random or adversarial “noise”) into learning and inference procedures can be beneficial. The focus was on two angles: first, on how stochastic perturbations can be used to construct new types of probability models for structured data; and second, how deterministic perturbations affect the regularization and the generalization properties of learning algorithms.

The goal of this workshop is to expand the scope of previous workshops and also explore different ways to apply perturbations within optimization and statistics to enhance and improve machine learning approaches. This year, we would like to look at exciting new developments related to the above core themes.

More generally, we shall specifically be interested in understanding the following issues:

Modeling: which models lend efficient learning by perturbations?

Regularization: whether randomness can be replaced by other mathematical object while keeping the computational and statistical guarantees?
* Robust optimization: how stochastic and adversarial perturbations affect the learning outcome?

* Dropout: How stochastic dropout regularizes online learning tasks?
* Sampling: how perturbation can be applied to sample from continuous spaces?

Author Information

Tamir Hazan (Technion)
George Papandreou (Toyota Technological Institute at Chicago)
Danny Tarlow (Google Brain)

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