This algorithm uses a minimally adequate Teacher (MAT) to pose questions about the unknown set.
[10] Her work addresses the problem of adapting learning algorithms to cope with incorrect training examples (noisy data).
Angluin's study demonstrates that algorithms exist for learning in the presence of errors in the data.
[10] In distributed computing, she co-invented the population protocol model and studied the problem of consensus.
[20][21] She organized Yale's Computer Science Department's Perlis Symposium in April 2001: "From Statistics to Chat: Trends in Machine Learning".