Learning from satisfying assignments under continuous distributions

SODA '20: ACM-SIAM Symposium on Discrete Algorithms Salt Lake City Utah January, 2020, pp. 82-101, 2020.

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Abstract:

What kinds of functions are learnable from their satisfying assignments? Motivated by this simple question, we extend the framework of [DDS15], which studied the learnability of probability distributions over {0, 1}n defined by the set of satisfying assignments to "low-complexity" Boolean functions, to Boolean-valued functions defined ove...More

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