ML@CL Group Meeting

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• Wenbo Gong, Microsoft
• Friday 04 November 2022, 16:00-17:00
• Computer Laboratory, William Gates Building, LT2.

If you have a question about this talk, please contact Aditya Ravuri.

Discovering causal relationships between different variables from time series data has been a long-standing challenge for many domains. Given the complexity of real-world relationships and the nature of observation in discrete time, the causal discovery method needs to consider non-linear relations between variables, instantaneous effects and history dependent noise. However, previous works do not offer a solution addressing all these problems together.

In the first part of this talk, we will first set the scene by covering the basic concepts of causality, together with an end-to-end deep learning based causal inference model called DECI . In the second part, we will present our solution towards addressing the aforementioned challenges in real-world time series data by extending DECI . We name it Rhino, which can model non-linear relationships with instantaneous effects while allowing the noise distribution to be modulated by historical observations.

This talk is part of the ML@CL Group Meetings series.

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• Computer Laboratory, William Gates Building, LT2
• ML@CL Group Meetings

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