Subject: Mathematics - Statistics Theory | Computer Science - Artificial Intelligence | Computer Science - Machine Learning | Computer Science - Data Structures and Algorithms
We investigate the problem of learning Bayesian networks in a robust model where an $\epsilon$-fraction of the samples are adversarially corrupted. In this work, we study the fully observable discrete case where the structure of the network is given. Even in this basic ... View more
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