Learning from satisfying assignments under continuous distributions

2 Jul 2019Clément L. CanonneAnindya DeRocco A. Servedio

What kinds of functions are learnable from their satisfying assignments? Motivated by this simple question, we extend the framework of De, Diakonikolas, and Servedio [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 over continuous domains... (read more)

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