Time-Space Tradeoffs for Learning from Small Test Spaces: Learning Low Degree Polynomial Functions

8 Aug 2017 Paul Beame Shayan Oveis Gharan Xin Yang

We develop an extension of recently developed methods for obtaining time-space tradeoff lower bounds for problems of learning from random test samples to handle the situation where the space of tests is signficantly smaller than the space of inputs, a class of learning problems that is not handled by prior work. This extension is based on a measure of how matrices amplify the 2-norms of probability distributions that is more refined than the 2-norms of these matrices... (read more)

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