OPT 2012 – NIPS Workshop on Optimization for Machine Learning

OPT 2012
NIPS Workshop on Optimization for Machine Learning
(Dec., 7-8th, 2012)
Visit: http://opt.kyb.tuebingen.mpg.de/index.html
Submit: http://www.easychair.org/conferences/?conf=opt2012

We invite participation in the 5th International Workshop on
“Optimization for
Machine Learning”, to be held as a part of the NIPS 2012 conference.

Join us for an exciting program that includes plenary talks by:

* Pablo Parrilo (MIT)
* Francis Bach (INRIA)

Research contributions from the community form an integral part of our
and we invite papers for oral and poster presentation in the workshop. We
encourage authors to not only submit finished pieces of work, but also works
currently in progress that you would like to announce and get feedback
on. Accepted submissions will have the option to be considered for a JMLR
special issue for the workshop proceedings.

To encourage cutting-edge participation, the workshop will offer a “best
presentation” award as recognition. We also encourage submissions describing
practical systems and softwares that have been implemented to address
optimization problems that arise in machine learning.

The main topics are, including, but not limited to:

* Stochastic, Parallel and Online Optimization,
– Large-scale learning, massive data sets
– Distributed algorithms
– Optimization on massively parallel architectures
– Optimization using GPUs, Streaming algorithms
– Decomposition for large-scale, message-passing and online learning
– Stochastic approximation
– Randomized algorithms

* Nonconvex Optimization
– Efficient nonsmooth global optimization
– Nonsmooth, nonconvex optimization
– Nonconvex quadratic programming, including binary QPs
– Convex Concave Decompositions, D.C. Programming, EM
– Training of deep architectures and large hidden variable models
– Approximation Algorithms

* Algorithms and Techniques (application oriented)
– Global and Lipschitz optimization
– Algorithms for nonsmooth optimization
– Linear and higher-order relaxations
– Polyhedral combinatorics applications to ML problems

* Combinatorial Optimization
– Optimization in Graphical Models
– Structure learning
– MAP estimation in continuous and discrete random fields
– Clustering and graph-partitioning
– Semi-supervised and multiple-instance learning

* Practical techniques
– Optimization software and toolboxes
– GPU, Multicore, Distributed implementations

* Applications close to machine learning
– Sparse learning, compressed sensing, signal processing
– Computational Statistics
– Large scale scientific computing

Important Dates

* Deadline for submission of papers: 28th September 2012
* Notification of acceptance: 25th October 2012
* Final version of submission: 5th November 2012

Please note that at least one author of each accepted paper must be
available to present the paper at the workshop. Further details
regarding the submission process are available at the workshop


* Suvrit Sra, Max Planck Institute for Intelligent Systems
* Alekh Agarwal, Microsoft Research New York
* Senior Advisor: Stephen Wright, University of Wisconsin, Madison