Towards Life-Long Meta Learning


We reformulate algorithm selection as a time allocation problem: all candidate algorithms are run in parallel, and their relative priorities are continually updated based on its current time to solution, estimated according to a parametric model that is trained and used while solving a sequence of problems. 


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