A Time-Space Lower Bound for a Large Class of Learning Problems

2017 IEEE 58TH ANNUAL SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE (FOCS), pp. 732-742, 2017.

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Abstract:

We prove a general memory-samples lower bound that applies for a large class of learning problems and shows that for every problem in that class, any learning algorithm requires either a memory of quadratic size or an exponential number of samples. Our result is stated in terms of the norm of the matrix that corresponds to the learning pr...More

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