[MLSys 07] Workshop on Statistical Learning Techniques for Solving Systems Problems

Collocated with the Twenty-First Annual Conference on Neural Information Processing Systems

**Dates:** December 7 and 8, 2007\\ **Location:** Hilton Meeting Room Sutcliffe B, Whistler, BC **Organizers:** *[[http://research.microsoft.com/~sumitb|Sumit Basu (Microsoft Research)]] *[[http://www.cs.berkeley.edu/~archanag|Archana Ganapathi (UC Berkeley)]] *[[http://research.microsoft.com/~emrek|Emre Kiciman (Microsoft Research)]] *[[http://research.yahoo.com/Fei_Sha|Fei Sha (Yahoo Research)]] **Contact:** \\ **{{nips_cfp.pdf|Call for Abstracts}}** (Abstracts were due Oct 31, 2007) **Click [[schedule|here]] for the schedule** **Click [[PostersInfo|here]] for the list of accepted posters** **Objective** In the last few years, there has been a budding interaction between machine learning and computer systems researchers. In particular, statistical machine learning techniques have found a wide range of successful applications in many core systems areas, from designing computer microarchitectures and analyzing network traffic patterns to managing power consumption in data centers and beyond. However, connecting these two areas has its challenges: while systems problems are replete with mountains of data and hidden variables, complex sets of interacting systems, and other exciting properties, labels can be hard to come by, and the measure of success can be hard to define. Furthermore, systems problems often require much more than high classification accuracy - the answers from the algorithms need to be both justifiable and actionable. Dedicated workshops in systems conferences have emerged (for example, SysML 2006 and SysML 2007) to address this area, though they have had little visibility to the machine learning community. A primary goal of this workshop is thus to expose these new research opportunities in systems areas to machine learning researchers, in the hopes of encouraging deeper and broader synergy between the two communities. During the workshop, through various planned overviews, invited talks, poster sessions, group discussions, and panels, we would like to achieve three objectives. First, we wish to discuss the unique opportunities and challenges that are inherent to this area. Second, we want to discuss and identify "low-hanging fruit" that are be more easily tackled using existing learning techniques. Finally, we will cover how researchers in both areas can make rapid progress on these problems using existing toolboxes for both machine learning and systems. We hope that this workshop will present an opportunity for intensive discussion of existing work in machine learning and systems, as well as inspire a new generation of researchers to become involved in this exciting domain.