COSTNIPS 2011 - Workshop on Computational Trade-offs in Statistical Learning

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Category COSTNIPS 2011

Deadline: October 17, 2011 | Date: December 12, 2011

Venue/Country: Granada, Spain

Updated: 2011-09-15 06:58:21 (GMT+9)

Call For Papers - CFP

NIPS 2011 Workshop, Sierra Nevada, Spain

https://sites.google.com/site/costnips/

-- Submission Deadline: October 17, 2011 --

OVERVIEW

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 and 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

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

* Shai Shalev-Shwartz

* Ben Taskar

SUBMISSION GUIDELINES

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

Alekh Agarwal

Alexander Rakhlin

PROGRAM COMMITTEE

Léon Bottou, Olivier Chapelle , John Duchi, Claudio Gentile, John

Langford, Maxim Raginsky, Pradeep Ravikumar, Ohad Shamir, Karthik

Sridharan, David Weiss