The Neural Information Processing Systems (NIPS) Foundation’s 2011 Conference (NIPS 2011) has announced a special two-day workshop on parallel and large-scale machine learning called Big Learning: Algorithms, Systems, and Tools for Learning at Scale. The workshop, collocated with NIPS 2011 in Granada, Spain, aims “to bring together parallel system builders in industry and academia, machine learning algorithms experts, and end users to identify the key challenges, opportunities, and myths of Big Learning.” There will be a focus on practical case studies, demos, benchmarks, and lessons-learned papers.

From the official call for papers:

This workshop will address tools, algorithms, systems, hardware, and real-world problem domains related to large-scale machine learning (“Big Learning”). The Big Learning setting has attracted intense interest with active research spanning diverse fields including machine learning, databases, parallel and distributed systems, parallel architectures, and programming languages and abstractions. This workshop will bring together experts across these diverse communities to discuss recent progress, share tools and software, identify pressing new challenges, and to exchange new ideas. Topics of interest include (but are not limited to):

 

• Hardware Accelerated Learning: Practicality and performance of specialized high-performance hardware (e.g. GPUs, FPGAs, ASIC) for machine learning applications.
• Applications of Big Learning: Practical application case studies; insights on end-users, typical data workflow patterns, common data characteristics (stream or batch); trade-offs between labeling strategies (e.g., curated or crowd-sourced); challenges of real-world system building.
• Tools, Software, & Systems: Languages and libraries for large-scale parallel or distributed learning. Preference will be given to approaches and systems that leverage cloud computing (e.g. Hadoop, DryadLINQ, EC2, Azure), scalable storage (e.g. RDBMs, NoSQL, graph databases), and/or specialized hardware (e.g. GPU, Multicore, FPGA, ASIC).
• Models & Algorithms: Applicability of different learning techniques in different situations (e.g., simple statistics vs. large structured models); parallel acceleration of computationally intensive learning and inference; evaluation methodology; trade-offs between performance and engineering complexity; principled methods for dealing with large number of features.

The deadline to submit a four-page extended abstract is Sept. 30, 2011. For author instructions, click here. For complete details about the workshop, click here.

And as a reminder, be sure to check out the CCC’s recent white paper series on data analytics, describing the challenges in data mining, machine learning, predictive modeling, etc., in the context of national priorities such as health, education, energy, transportation, and national defense.

(Contributed by Erwin Gianchandani, CCC Director, and Eric Horvitz, Microsoft Research)