Kun Zhang: Learning and Using Causal Representations
"Learning and Using Causal Representations" Kun Zhang, Carnegie Mellon University Discussant: Cosma Shalizi: Carnegie Mellon University Abstract: When do we have to make use of causal knowledge, and when does associational information suffice for machine learning? Can we find the causal direction between two variables by analyzing their observed values? Can we figure out where latent causal variables should be and how they are related? For the purpose of understanding and manipulating systems properly, people often attempt to answer such causal questions. Furthermore, we are often concerned with artificial intelligence (AI) in complex environments. For instance, how can we do transfer learning in a principled way? How can machines deal with adversarial attacks? Interestingly, it has recently been shown that causal information can facilitate understanding and solving various AI problems. This talk focused on how to learn (hidden) causal representations from observation data and why and how the causal perspective allows adaptive prediction and a potentially higher level of artificial intelligence. March 16, 2021
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