NIPS 2012 Workshophttp://www.biglearn.org
- Sameer Singh email@example.com (UMass Amherst)
- John Duchi firstname.lastname@example.org (UC Berkeley)
- Yucheng Low email@example.com (Carnegie Mellon University)
- Joseph Gonzalez firstname.lastname@example.org (UC Berkeley)
Submissions are solicited for a one day workshop on December 7-8 in Lake Tahoe, Nevada.
This workshop will address algorithms, systems, and real-world problem domains related to large-scale machine learning (“Big Learning”). With active research spanning machine learning, databases, parallel and distributed systems, parallel architectures, programming languages and abstractions, and even the sciences, Big Learning has attracted intense interest. 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):
Big Data: Methods for managing large, unstructured, and/or streaming data; **cleaning, visualization, interactive platforms for data understanding and **interpretation; sketching and summarization techniques; sources of large **datasets.
Models & Algorithms: Machine learning algorithms for parallel, distributed, **GPGPUs, or other novel architectures; theoretical analysis; distributed online **algorithms; implementation and experimental evaluation; methods for **distributed fault tolerance.
Applications of Big Learning: Practical application studies and challenges **of real-world system building; insights on end-users, common data **characteristics (stream or batch); trade-offs between labeling strategies **(e.g., curated or crowd-sourced).
Tools, Software & Systems: Languages and libraries for large-scale parallel **or distributed learning which leverage cloud computing, scalable storage (e.g. **RDBMs, NoSQL, graph databases), and/or specialized hardware.
Submissions should be written as extended abstracts, no longer than 4 pages (excluding references) in the NIPS latex style. Relevant work previously presented in non-machine-learning conferences is strongly encouraged, though submitters should note this in their submission.
Submission Deadline: October 17th, 2012. Please refer to the website for detailed submission instructions: http://biglearn.org