Learning Sparse Multivariate Polynomials over a Field with Queries and Counterexamples
We consider the problem of learning a polynomial over an arbitrary eld F de ned on a set of boolean variables. We present the rst provably e ective algorithm for exactly identifying such polynomials using membership and equivalence queries. Our algorithm runs in time polynomial in n, the number of variables, and t, the number of nonzero terms appearing in the polynomial. The algorithm makes at most nt+ 2 equivalence queries, and at most (nt+ 1)(t 2 + 3t)=2 membership queries. Our algorithm is equally e ective for learning a generalized type of polynomial de ned on certain kinds of semilattices. We also present an extension of our algorithm for learning multilinear polynomials when the domain of each variable is the entire eld F .