Big Learning : Advances in Algorithms and Data Management

Explosive growth in data and availability of cheap computing resources has sparked increasing interest in Big Learning within the Machine Learning community. Researchers are now taking on the challenge of parallelizing richly structured models with inherently serial dependencies and do not admit straightforward solutions.

Database researchers, however, have a history of developing high performance systems that allow concurrent access while providing theoretical guarantees on correctness. In recent years, database systems have been developed specifically to tackle Big Learning tasks.

This workshop aims to bring together the two communities and facilitate the cross-pollination of ideas. Rather than passively using DB systems, ML researchers can apply major DB concepts to their work; DB researchers stand to gain an understanding of the ML challenges and better guide the development of their Big Learning systems.

The goals of the workshop are
- Identify challenges faced by ML practitioners in Big Learning setting
- Showcase recent and ongoing progress towards parallel ML algorithms
- Highlight recent and significant DB research in addressing Big Learning problems
- Introduce DB implementations of Big Learning systems, and the principle considerations and concepts underlying their designs

Focal points for discussions and solicited submissions include but are not limited to:
- Scalable data systems for Big Learning --- models and algorithms implemented, properties (availability, consistency, scalability, etc.), strengths and limitations
- Distributed algorithms for online and batch learning
- Parallel (multicore) algorithms for online and batch learning
- Theoretical analysis of distributed and parallel learning algorithms
- Implementation studies of large-scale distributed inference and learning algorithms --- challenges faced and lessons learnt

Target audience includes industry and academic researchers from the various subfields relevant to large-scale machine learning, with a strong bias for either position talks that aim to induce discussion, or accessible overviews of the state-of-the-art.