OVERVIEW
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Since its early days, the field of Machine Learning has focused on developing computationally 
tractable algorithms with good learning guarantees. The vast literature on statistical learning 
theory has led to a good understanding of how the predictive performance of different algorithms 
improves as a function of the number of training samples. By the same token, the well-developed 
theories of optimization and sampling methods have yielded efficient computational techniques at 
the core of most modern learning methods. The separate developments in these fields mean that 
given an algorithm we have a sound understanding of its statistical and computational behavior. 
However, there hasn't been much joint study of the computational and statistical complexities of 
learning, as a consequence of which, little is known about the interaction and trade-offs between 
statistical accuracy and computational complexity. Indeed a systematic joint treatment can answer 
some very interesting questions: what is the best attainable statistical error given a finite 
computational budget? What is the best learning method to use given different computational 
constraints and desired statistical yardsticks? Is it the case that simple methods outperform 
complex ones in computationally impoverished scenarios?

The goal of our workshop is to draw the attention of machine learning researchers to this rich an
emerging area of problems and to establish a community of researchers that are interested in 
understanding computational and statistical trade-offs. We aim to define a number of common 
problems in this area and to encourage future research.


TOPICS
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We would like to welcome high-quality submissions on topics including but not limited to:

* Fundamental statistical limits with bounded computation
* Trade-offs between statistical accuracy and computational costs
* Computation-preserving reductions between statistical problems
* Algorithms to learn under budget constraints
* Budget constraints on other resources (e.g. bounded memory)
* Computationally aware approaches such as coarse-to-fine learning

Interesting submissions in other relevant topics not listed above are welcome too. Due to the time 
constraints, most accepted submissions will be presented as poster spotlights.


INVITED SPEAKERS
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* Shai Shalev-Shwartz
* Ben Taskar


SUBMISSION GUIDELINES
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Submissions should be written as extended abstracts, no longer than 4 pages in the NIPS latex 
style. NIPS style files and formatting instructions can be found at 
http://nips.cc/PaperInformation/StyleFiles. The submissions should include the authors' name and 
affiliation since the review process will not be double blind. The extended abstract may be 
accompanied by an unlimited appendix and other supplementary material, with the understanding 
that anything beyond 4 pages may be ignored by the program committee. The papers can be submitted 
at https://sites.google.com/site/costnips/submission by Oct 17, 5PM PST.
Authors will be notified on or before Nov 4.

ORGANIZERS
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Alekh Agarwal
Alexander Rakhlin


PROGRAM COMMITTEE
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Léon Bottou, Olivier Chapelle , John Duchi, Claudio Gentile, John Langford, Maxim Raginsky, 
Pradeep Ravikumar, Ohad Shamir, Karthik Sridharan, David Weiss