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Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift

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Chelsea Finn: Supervised ML methods (i.e. ERM) assume that train & test data are from the same distribution, & deteriorate when this assumption is broken. To help, we introduce adaptive risk minimization (ARM): https://arxiv.org/abs/2007.02931 With M Zhang, H Marklund @abhishekunique7 @svlevine (1/6)

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Sergey Levine: In the real world, the test distribution never actually matches the training distribution. Adaptive risk minimization (ARM) addresses distributional shift by adapting to it, without labels -- just from seeing a group of test inputs rather than a single individual test input. https://t.co/vOrVzw4pDX

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Found on Jul 07 2020 at https://arxiv.org/pdf/2007.02931.pdf

PDF content of a computer science paper: Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift