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Auditing and Achieving Intersectional Fairness in Classification Problems
Machine learning algorithms are extensively used to make increasingly more consequential decisions about people, so achieving optimal predictive performance can no longer be the only focus.
Complexity of Equivalence and Learning for Multiplicity Tree Automata
Habrard and Oncina (2006) give an exact learning algorithm for multiplicity tree automata, in which the number of queries is proportional to the size of the target automaton and the size of a largest counterexample, represented as a tree, that is returned by the Teacher.
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