Determining membership with 2 simultaneous queries

Learning Using Local Membership Queries

We introduce a new model of membership query (MQ) learning, where the learning algorithm is restricted to query points that are close to random examples drawn from the underlying distribution. The learning model is intermediate between the PAC model (Valiant, 1984) and the PAC+MQ model (where the queries are allowed to be arbitrary points). Membership query algorithms are not popular among mach...

On Using Extended Statistical Queries to Avoid Membership Queries

The Kushilevitz-Mansour (KM) algorithm is an algorithm that finds all the “large” Fourier coefficients of a Boolean function. It is the main tool for learning decision trees and DNF expressions in the PAC model with respect to the uniform distribution. The algorithm requires access to the membership query (MQ) oracle. The access is often unavailable in learning applications and thus the KM algo...

On Distribution-Specific Learning with Membership Queries versus Pseudorandom Generation

Learning Minor Closed Graph Classes with Membership and Equivalence Queries

The paper considers the problem of learning classes of graphs closed under taking minors It is shown that any such class can be properly learned in polynomial time using membership and equivalence queries The representation of the class is in terms of a set of minimal excluded minors obstruction set

Learning Boolean Halfspaces with Small Weights from Membership Queries

We consider the problem of proper learning a Boolean Halfspace with integer weights {0, 1, . . . , t} from membership queries only. The best known algorithm for this problem is an adaptive algorithm that asks n ) membership queries where the best lower bound for the number of membership queries is n [4]. In this paper we close this gap and give an adaptive proper learning algorithm with two rou...