Invited Talk 1:
Open Science in Machine Learning
Cheng Soon Ong
Abstract. Openness and unrestricted information sharing amongst scientists have been
identified as values that are critical to scientific progress. Open science for
empirical machine learning has three main ingredients: open source software, open
access to results and open data. We discuss the current state of open source software
in machine learning based on our experience with mloss.org as well as the software track
in JMLR. Then we focus our attention on the question of open data and the design of a
proposed data repository that is community driven and scalable.
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