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SUMMARY:ML@CL Group Meeting - Wenbo Gong\, Microsoft
DTSTART:20221104T160000Z
DTEND:20221104T170000Z
UID:TALK192377@talks.cam.ac.uk
CONTACT:Aditya Ravuri
DESCRIPTION:Discovering causal relationships between different variables f
 rom time series data has been a long-standing challenge for many domains. 
 Given the complexity of real-world relationships and the nature of observa
 tion in discrete time\, the causal discovery method needs to consider non-
 linear relations between variables\, instantaneous effects and history dep
 endent noise. However\, previous works do not offer a solution addressing 
 all these problems together. \n\nIn 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 th
 e aforementioned challenges in real-world time series data by extending DE
 CI. We name it Rhino\, which can model non-linear relationships with insta
 ntaneous effects while allowing the noise distribution to be modulated by 
 historical observations.
LOCATION:Computer Laboratory\, William Gates Building\, LT2
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