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:
Papers submitted to the workshop should be up to four pages long (excluding references), extended abstracts in camera-ready format using the NIPS style. They should be uploaded (.pdf, up to 5MB) to CMT. Accepted submissions will be presented as talks or posters.
Participants should refer to the NIPS-2016 website for information on how to register for the workshop.
The workshop will be a one day workshop. As with last year's workshop, the workshop will consist of 6-8 invited and contributed talks, with a poster session. The posters should be 36 x 48 inches (portrait format).
Modern Nonparametric Methods in Machine Learning (NIPS-2014)
Modern Nonparametric Methods in Machine Learning (NIPS-2013)
Modern Nonparametric Methods in Machine Learning (NIPS-2012)
If you have any question or comment, feel free to contact us (''firstname.lastname@example.org'').