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News:

• Workshop videos are now available here.
• Congratulations to Felipe Tobar, Thang Bui and Richard Turner whose paper "Design of Covariance Functions using Inter-Domain Inducing Variables" won the Best paper award!
• Thank you to all the workshop participants. We hope you have enjoyed it as much as we did!

Overview:

Data, in the form of time-dependent sequential observations emerge in many key real-world problems ranging from biological data, to financial markets, to weather forecasting and audio/video processing. However, despite the ubiquity of such data, the vast majority of learning algorithms have been primarily developed for the setting in which sample points are drawn i.i.d. from some possibly unknown fixed distribution. While there exist algorithms designed to handle non-i.i.d. data, these typically assume specific parametric form of data-generating distribution. Such assumptions may undermine the possibly complex nature of modern data which can possess long-range dependency patterns that we now have the computing power to discern. On the other extreme are on-line learning algorithms that consider a more general framework without any distributional assumptions. However, common on-line algorithms may not fully address the stochastic aspect of time-series data.