======================= Learning with Curricula ======================= - Joseph O'Sullivan I have been interested in improving learning accuracy when multiple functions are learned over time and particularly interested in exploiting some notion of curriculum in such scenarios. Three coupled questions arise: (1) what algorithms can be developed that benefit from previous learning experience when presented with a new function to be learned? (2) what curriculum, or ordering of functions, produces the most effective learning? and (3) how should such curricula be used? In this talk, I'll define "Learning with Curricula" and introduce a notation for discussing it. I'll present two specific algorithms that learn with curricula, SMTL and STINT. SMTL is an extension of MTL that allow us to transfer knowledge sequentially from multiple previously learned functions by utilizing the hidden representations constructed by previously learned functions in the new tasks. STINT combines multiple artificial neural networks in a directed acyclic graph, so as to benefit from previously learned functions in curricula. Given a set of tasks, and labeled data for each task, we show that simple greedy algorithms can generate a curriculum that approximates the optimal curriculum. I'll then talk about how this notion of curricula occurs naturally in situations such as robot learning, and will present results testing these algorithms in a mobile robot domain, showing that learning with a curriculum can significantly reduce the number of examples required to learn particular novel tasks.