OPT 2009: 2nd NIPS Workshop on Optimization for Machine Learning

It is fair to say that at the heart of every machine learning algorithm is an optimization problem. It is only recently that this viewpoint has gained significant following. Classical optimization techniques based on convex optimization have occupied center-stage due to their attractive theoretical properties. But, new non-smooth and non-convex problems are being posed by machine learning paradigms such as structured learning and semi-supervised learning. Moreover, machine learning is now very important for real-world problems which often have massive datasets, streaming inputs, and complex models that also pose significant algorithmic and engineering challenges. In summary, machine learning not only provides interesting applications but also challenges the underlying assumptions of most existing optimization algorithms.

Therefore, there is a pressing need for optimization "tuned" to the machine learning context. For example, techniques such as non-convex optimization (for semi-supervised learning), combinatorial optimization and relaxations (structured learning), non-smooth optimization (sparsity constraints, L1, Lasso, structure learning), stochastic optimization (massive datasets, noisy data), decomposition techniques (parallel and distributed computation), and online learning (streaming inputs) are relevant in this setting. These techniques naturally draw inspiration from other fields, such as operations research, theoretical computer science, and the optimization community.

Motivated by these concerns, we would like to address these issues in the framework of this workshop.

Background and Objectives

This workshop is in continuation to the successful PASCAL2 Workshop on Optimization for Machine Learning, which was held at NIPS*2008, in Whistler, Canada, and was very well-received with packed attendence almost throughout the day.

Other workshops, such as 'Mathematical Programming in Machine Learning / Data Mining' held from 2005--2007 also share the spirit of our workshop. These workshops were quite extensive and provided a solid platform for encouraging exchange between machine learners and optimization researchers. Another relevant workshop was the BigML NIPS*2007 workshop that focused on algorithmic challeges faced for large-scale machine learning tasks, with a focus on parallelization or online learning.

Our workshop addresses the following major issues, some of which have not been previously tackled as a combined optimization and machine learning effort. In particular, the main aims of our workshop are

• Bring together experts from machine learning, optimization, operations research, and statistics to further an exchange of ideas and techniques
• Focus on problems of interest to the NIPS audience
• Identify a set of important open problems and issues that lie at the intersection of both machine learning and optimization

NEWS

Please Register!
Location: Hilton, Sutcliffe B
07 Sep. 2009 -- Website goes online
12 Sep. 2009 -- Submission website open
24 Oct. 2009 -- Submission closed
25 Oct. 2009 -- Paper review started
06 Nov. 2009 -- Review period ended
08 Nov. 2009 -- Paper Notifications out
09 Nov. 2009 -- Prelim schedule up
23 Nov. 2009 -- Papers PDFs are online
23 Nov. 2009 -- Schedule is online
08 Feb. 2010 -- Videos linked, Schedule updated

We gratefully acknowledge the support of: