Baihan Lin, Guillermo Cecchi, et al.
IJCAI 2023
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.
Baihan Lin, Guillermo Cecchi, et al.
IJCAI 2023
Imran Nasim, Melanie Weber
SCML 2024
Saeel Sandeep Nachane, Ojas Gramopadhye, et al.
EMNLP 2024
Yidi Wu, Thomas Bohnstingl, et al.
ICML 2025