Interactive Learning from Multiple Noisy Labels

We consider binary classification problems in the presence of a teacher, who acts as an intermediary to provide a learning algorithm with meaningful, well-chosen examples. This setting is also known as curriculum learning [1, 2, 3] or self-paced learning [4, 5, 6] in the literature. Existing practical methods [4, 7] that employ such a teacher operate by providing the learning algorithm with easy examples first and then progressively moving on to more difficult examples. Such a strategy is known to improve the generalization ability of the learning algorithm and/or alleviate local minima problems while optimizing non-convex objective functions. In this work, we propose a new method to quantify the notion of easiness of a training example.