Call for Contributions: Resource-Efficient Machine Learning, NIPS-2013 workshop

Call for Contributions: Resource-Efficient Machine Learning, NIPS-2013 workshop

Posted by Rebecca Martin on Wed, 28/08/2013 - 10:26

CALL FOR ABSTRACTS AND OPEN PROBLEMS
Resource-Efficient Machine Learning
NIPS-2013 Workshop
Tuesday, December 10, 2012
Lake Tahoe, Nevada, US
https://sites.google.com/site/resefml2013
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We invite submission of abstracts and open problems to Resource-Efficient Machine Learning NIPS-2013 workshop.
IMPORTANT DATES
Submission Deadline: October 9.
Notification of Acceptance: October 23.
More details are provided below.
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Abstract
Resource efficiency is key for making ideas practical. It is crucial in many tasks, ranging from large-scale learning ("big data'') to small-scale mobile devices. Understanding resource efficiency is also important for understanding biological systems, from individual cells to complex learning systems, such as the human brain. The goal of this workshop is to improve our fundamental theoretical understanding and link between various applications of learning under constraints on the resources, such as computation, observations, communication, and memory. While the founding fathers of machine learning were mainly concerned with characterizing the sample complexity of learning (the observations resource) [VC74] it now gets realized that fundamental understanding of other resource requirements, such as computation, communication, and memory is equally important for further progress [BB11].

The problem of resource-efficient learning is multidimensional and we already see some parts of this puzzle being assembled. One question is the interplay between the requirements on different resources. Can we use more of one resource to save on a different resource? For example, the dependence between computation and observations requirements was studied in [SSS08,SSST12,SSB12]. Another question is online learning under various budget constraints [AKKS12,BKS13,CKS04,DSSS05,CBG06]. One example that Badanidiyuru et al. provide is dynamic pricing with limited supply, where we have a limited number of items to sell and on each successful sale transaction we lose one item. A related question of online learning under constraints on information acquisition was studied in [SBCA13], where the constraints could be computational or monetary. Yet another direction is adaptation of algorithms to the complexity of operation environment. Such adaptation can allow resource consumption to reflect the hardness of a situation being faced. An example of such adaptation in multiarmed bandits with side information was given in [SAL+11]. Another way of adaptation is interpolation between stochastic and adversarial environments. At the moment there are two prevailing formalisms for modeling the environment, stochastic and adversarial (also known as ``the average case'' and ``the worst case''). But in reality the environment is often neither stochastic, nor adversarial, but something in between. It is, therefore, crucial to understand the intermediate regime. First steps in this direction were done in [BS12]. And, of course, one of the flagman problems nowadays is ``big data'', where the constraint shifts from the number of observations to computation. We strongly believe that there are deep connections between problems at various scales and with various resource constraints and there are basic principles of learning under resource constraints that are yet to be discovered. We invite researchers to share their practical challenges and theoretical insights on this problem.

Study of resource-efficient learning also require design of resource-dependent performance measures. In the past, algorithms were compared in terms of predictive accuracy (classification errors, AUC, F-measures, NDCG, etc.), yet there is a need to evaluate them with additional metrics related to resources, such as memory, CPU time, and even power. For example, reward per computational operation. This theme will also be discussed at the workshop.

References:
[AKKS12] Kareem Amin, Michael Kearns, Peter Key and Anton Schwaighofer. Budget Optimization for Sponsored Search: Censored Learning in MDPs. UAI 2012.
[BB11] Leon Bottou and Olivier Bousquet. The trade-offs of large scale learning. In Suvrit Sra, Sebastian Nowozin, and Stephen J. Wright, editors, Optimization for Machine Learning. MIT Press, 2011.
[BKS13] Ashwinkumar Badanidiyuru, Robert Kleinberg and Aleksandrs Slivkins. Bandits with Knapsacks. FOCS, 2013.
[BS12] Sebastien Bubeck and Aleksandrs Slivkins. The best of both worlds: stochastic and adversarial bandits. COLT, 2012.
[CBG06] Nicolò Cesa-Bianchi and Claudio Gentile. Tracking the best hyperplane with a simple budget perceptron. COLT 2006.
[CKS04] Koby Crammer, Jaz Kandola and Yoram Singer. Online Classification on a Budget. NIPS 2003.
[DSSS05] Ofer Dekel, Shai Shalev-shwartz and Yoram Singer. The Forgetron: A kernel-based perceptron on a fixed budget. NIPS 2004.
[SAL+11] Yevgeny Seldin, Peter Auer, François Laviolette, John Shawe-Taylor, and Ronald Ortner. PAC-Bayesian Analysis of Contextual Bandits. NIPS, 2011.
[SBCA13] Yevgeny Seldin, Peter Bartlett, Koby Crammer, and Yasin Abbasi-Yadkori. Prediction with Limited Advice and Multiarmed Bandits with Paid Observations. 2013.
[SSB12] Shai Shalev-Shwartz and Aharon Birnbaum. Learning halfspaces with the zero-one loss: Time-accuracy trade-offs. NIPS, 2012.
[SSS08] Shai Shalev-Shwartz and Nathan Srebro. SVM Optimization: Inverse Dependence on Training Set Size. ICML, 2008.
[SSST12] Shai Shalev-Shwartz, Ohad Shamir, and Eran Tromer. Using more data to speed-up training time. AISTATS, 2012.
[VC74] Vladimir N. Vapnik and Alexey Ya. Chervonenkis. Theory of pattern recognition. Nauka, Moscow (in Russian), 1974. German translation: W.N.Wapnik, A.Ya.Tschervonenkis (1979), Theorie der Zeichenerkennug, Akademia, Berlin.

Call for Sponsors
Your logo could be here.... If you are interested to sponsor this event, please, contact yevgeny.seldin at gmail.

Call for Contributions
We invite submission of abstracts and open problems to the workshop. Abstracts and open problems should be at most 4 pages long in the NIPS format. Appendices are allowed, but the organizers reserve the right to evaluate the submissions based on the first 4 pages only. Submissions should be NOT anonymous. Selected abstracts and open problems will be presented as talks or posters during the workshop. Contributions should be emailed to yevgeny.seldin at gmail.

IMPORTANT DATES
Submission Deadline: October 9.
Notification of Acceptance: October 23.

EVALUATION CRITERIA
• Theory and application-oriented contributions are equally welcome.
• All submissions should emphasize relevance to the workshop subject.
• Submission of previously published work or work under review is allowed, in particular NIPS-2013 submissions. However, for oral presentations preference will be given to novel work or work that was not yet presented elsewhere (for example, recent journal publications or NIPS posters). All double submissions must be clearly declared as such!

Invited Speakers (tentative)
Alexandrs Slivkins, Microsoft Research
Michael Mahoney, Stanford
Organizers
Yevgeny Seldin, Queensland University of Technology and UC Berkeley
Koby Crammer, The Technion
Yasin Abbasi-Yadkori, Queensland University of Technology and UC Berkeley
Ralf Herbrich, Amazon
Peter Bartlett, UC Berkeley and Queensland University of Technology
Schedule
TBA