Adaptive and Scalable Nonparametric Methods in Machine Learning

Large amounts of high-dimensional data are routinely acquired in scientific fields ranging from biology, genomics and health sciences to astronomy and economics due to improvements in engineering and data acquisition techniques. Nonparametric methods allow for better modelling of complex systems underlying data generating processes compared to traditionally used linear and parametric models. From statistical point of view, scientists have enough data to reliably fit nonparametric models. However, from computational point of view, nonparametric methods often do not scale well to big data problems.

The aim of this workshop is to bring together practitioners, who are interested in developing and applying nonparametric methods in their domains, and theoreticians, who are interested in providing sound methodology. We hope to effectively communicate advances in development of computational tools for fitting nonparametric models and discuss challenging future directions that prevent applications of nonparametric methods to big data problems.

We encourage submissions on a variety of topics, including but not limited to:
- Randomized procedures for fitting nonparametric models. For example, sketching, random projections, core set selection, etc.
- Nonparametric probabilistic graphical models
- Scalable nonparametric methods
- Multiple kernel learning
- Random feature expansion
- Novel applications of nonparametric methods
- Bayesian nonparametric methods
- Nonparametric network models

This workshop is a fourth in a series of NIPS workshops on modern nonparametric methods in machine learning. Previous workshops focused on time/accuracy tradeoffs, high dimensionality and dimension reduction strategies, and automating the learning pipeline.