User:Spanini/sandbox

Research

L* Algorithm

Angluin has written highly cited papers on computational learning theory, particularly in the context of learning regular language sets from membership and equivalence queries using the L* algorithm. In essence, this algorithm is a way for programs to learn complex systems through the process of trial and error of educated guesses, to determine the behavior the system. THROUGH THE RESPONSES.... RELOCATE This algorithm addresses the problem of identifying an unknown set through the use of a minimally adequate Teacher (MAT). The MAT provides yes or no answers to membership queries, SAYING whether an input is a member of the unknown set, and equivalence queries, saying whether a description of the set is accurate or not. The Learner uses responses from the Teacher to refine its understanding of the set S in polynomial time. Though Angluin's paper was published in 1987, a 2017 article by computer science Professor Frits Vaandrager says "the most efficient learning algorithms that are being used today all follow Angluin's approach of a minimally adequate teacher".

Learning from Noisy Examples

Angluin's work on learning from noisy examples has also been very influential to the field of machine learning. Her work addresses the problem of adapting learning algorithms to cope with incorrect training examples (noisy data). Angluin's study shows that algorithms exist for learning in the presence of noisy data in non-trivial domains and propose how the ideas might be used in more general settings.

Other Works

In distributed computing, she co-invented the population protocol model and studied the problem of consensus. In probabilistic algorithms, she has studied randomized algorithms for Hamiltonian circuits and matchings.