A Lower Bound for Agnostically Learning Disjunctions

Adam R. Klivans, Alexander A. Sherstov

Abstract: We prove that the concept class of disjunctions cannot be pointwise approximated by linear combinations of any small set of arbitrary real-valued functions. That is, suppose there exist functions $\phi_1,\ldots,\phi_r:\{-1,1\}^n\to\Re$ with the property that every disjunction f on n variables has $\|f-\sum_{i=1}^r\alpha_i\phi_i\|_\infty\leq 1/3$ for some reals α 1,...,α r . We prove that then $r \geq 2^{\Omega(\sqrt{n})}.$ This lower bound is tight. We prove an incomparable lower bound for the concept class of linear-size DNF formulas. For the concept class of majority functions, we obtain a lower bound of Ω(2 n /n), which almost meets the trivial upper bound of 2 n for any concept class. These lower bounds substantially strengthen and generalize the polynomial approximation lower bounds of Paturi and show that the regression-based agnostic learning algorithm of Kalai et al. is optimal. Our techniques involve a careful application of results in communication complexity due to Razborov and Buhrman et al.