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Discriminant Learning Machines

Abstract : The cause-effect pair challenge has, for the first time, formulated the cause-effect problem as a learning problem in which a causation coefficient is trained from data. This can be thought of as a kind of meta learning. This chapter will present an overview of the contributions in this domain and state the advantages and limitations of the method as well as recent theoretical results (learning theory/mother distribution). This chapter will point to code from the winners of the cause-effect pair challenge.
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Submitted on : Monday, June 15, 2020 - 10:29:24 AM
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Diviyan Kalainathan, Olivier Goudet, Michèle Sebag, Isabelle Guyon. Discriminant Learning Machines. Guyon, Isabelle; Statnikov, Alexander; Batu, Berna Bakir. Cause Effect Pairs in Machine Learning, Springer Verlag, pp.155-189, 2019, The Springer Series on Challenges in Machine Learning, 978-3-030-21809-6. ⟨10.1007/978-3-030-21810-2_4⟩. ⟨hal-02433203⟩



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