Paper tackles label noise in datasets, proposing a robust learning algorithm.
problem Real-world datasets often have label noise.
method Introduces distilled examples and proposes a learning algorithm with theoretical guarantees.
result Classifiers on distilled examples converge to the Bayes optimal classifier under certain conditions.