Learning &mgr;-branching programs with queries
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
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Journal of Computer and System Sciences - Special issue: papers from the 22nd ACM symposium on the theory of computing, May 14–16, 1990
On learning bounded-width branching programs
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Property testing and its connection to learning and approximation
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Query learning of bounded-width OBDDs
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ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
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On testing computability by small width OBDDs
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Lower bounds for testing computability by small width OBDDs
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APPROX'11/RANDOM'11 Proceedings of the 14th international workshop and 15th international conference on Approximation, randomization, and combinatorial optimization: algorithms and techniques
Testing computability by width-two OBDDs
Theoretical Computer Science
Testing computability by width-2 OBDDs where the variable order is unknown
CIAC'10 Proceedings of the 7th international conference on Algorithms and Complexity
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Property testing is concerned with deciding whether an object (e.g. a graph or a function) has a certain property or is "far" (for some definition of far) from every object with that property. In this paper we give lower and upper bounds for testing functions for the property of being computable by a read-once width-2 Ordered Binary Decision Diagram (OBDD), also known as a branching program , where the order of the variables is known. Width-2 OBDDs generalize two classes of functions that have been studied in the context of property testing - linear functions (over GF (2)) and monomials. In both these cases membership can be tested in time that is linear in 1/*** . Interestingly, unlike either of these classes, in which the query complexity of the testing algorithm does not depend on the number, n , of variables in the tested function, we show that (one-sided error) testing for computability by a width-2 OBDD requires ***(log(n )) queries, and give an algorithm (with one-sided error) that tests for this property and performs $\tilde{O}(\log(n)/\epsilon)$ queries.