Extractor-Based Time-Space Lower Bounds for Learning
STOC '18: Symposium on Theory of Computing Los Angeles CA USA June, 2018, pp. 990-1002, 2018.
EI
Keywords:
Branching programstime-space tradeoffextractorslearning from samples
Abstract:
A matrix M: A × X → {−1,1} corresponds to the following learning problem: An unknown element x ∈ X is chosen uniformly at random. A learner tries to learn x from a stream of samples, (a1, b1), (a2, b2) …, where for every i, ai ∈ A is chosen uniformly at random and bi = M(ai,x).
Assume that k, l, r are such that any submatrix of M of at ...More
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