Ken C.L. Wong, Satyananda Kashyap, et al.
Pattern Recognition Letters
We present results concerning the learning of Monotone DNF (MDNF) from Incomplete Membership Queries and Equivalence Queries. Our main result is a new algorithm that allows efficient learning of MDNF using Equivalence Queries and Incomplete Membership Queries with probability of p = 1 - 1/poly(n, t) of failing. Our algorithm is expected to make O((tn/1 - p)2) queries, when learning a MDNF formula with t terms over n variables. Note that this is polynomial for any failure probability p = 1 - 1/poly(n, t). The algorithm's running time is also polynomial in t, n, and 1/(1 - p). In a sense this is the best possible, as learning with p = 1 - 1/ω(poly(n, t)) would imply learning MDNF, and thus also DNF, from equivalence queries alone.
Ken C.L. Wong, Satyananda Kashyap, et al.
Pattern Recognition Letters
Xiaoxiao Guo, Shiyu Chang, et al.
AAAI 2019
Baihan Lin, Guillermo Cecchi, et al.
IJCAI 2023
Arthur Nádas
IEEE Transactions on Neural Networks