Connectionists: CFP: NIPS 2016 Workshop on Artificial Intelligence for Data Science

[Charles Sutton]

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CALL FOR PAPERS

NIPS 2016 Workshop on Artificial Intelligence for Data Science 
(AI4DataSci)
Saturday 10 December 2016, Barcelona, Spain

Submission deadline: 1 November 2016
http://workshops.inf.ed.ac.uk/nips2016-ai4datasci/

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We invite researchers to submit recent work on artificial intelligence 
methods to support the practical process of data analytics. Submissions 
should take the form of extended abstracts of approximately two pages in 
NIPS format. Please see workshop web site (above) for submission 
instructions.

WORKSHOP DESCRIPTION

Machine learning methods have been applied beyond their origins in 
artificial intelligence to a wide variety of data analysis problems in 
fields such as science, health care, technology, and commerce. Previous 
research in machine learning, perhaps motivated by its roots in AI, has 
primarily aimed at fully-automated approaches for prediction problems. 
But predictive analytics is only one step in the larger pipeline of data 
science, which includes data wrangling, data cleaning, exploratory 
visualization, data integration, model criticism and revision, and 
presentation of results to domain experts.

An emerging strand of work aims to address all of these challenges in 
one stroke is by automating a greater portion of the full data science 
pipeline. This workshop will bring together experts in machine learning, 
data mining, databases and statistics to discuss the challenges that 
arise in the full end-to-end process of collecting data, analysing data, 
and making decisions and building new methods that support, whether in 
an automated or semi-automated way, more of the full process of 
analysing real data.

Considering the full process of data science raises interesting 
questions for discussion, such as: What aspects of data analysis might 
potentially be automated and what aspects seem more difficult? 
Statistical model building often emphasizes interpretability and human 
understanding, while machine learning often emphasizes predictive 
modeling --- are ML methods truly suitable for supporting the full data 
analysis pipeline? Do recent advances in ML offer help here? Finally, 
are there low hanging fruit, i.e., how much time is wasted on routine 
tasks in scientific data analysis that could be automated?

Specific topics of interest include: data cleaning, exploratory data 
analysis, semi-supervised learning, active learning, interactive machine 
learning, model criticism, automated and semi-automated model 
construction, usable machine learning, interpretable prediction methods 
and automatic methods to explain predictions. We are especially 
interested in contributions that take a broader perspective, i.e., that 
aim toward supporting the process of data science more holistically.

CONFIRMED SPEAKERS

Thomas Dietterich, Oregon State University
Carlos Guestrin, University of Washington
Others TBC

WORKSHOP ORGANIZERS

James Geddes, The Alan Turing Institute
Zoubin Ghahramani, Cambridge University
Padhraic Smyth, University of California Irvine
Charles Sutton, University of Edinburgh
Chris Williams, University of Edinburgh

KEY DATES

Paper submission: 1 November 2016
Acceptance notification: 16 November 2016
Workshop: 10 December 2016

-- 
Charles Sutton * Reader in Machine Learning * University of Edinburgh
Director, EPSRC CDT in Data Science * http://datascience.inf.ed.ac.uk/
Faculty Fellow, Alan Turing Institute * http://turing.ac.uk/

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