Human intelligence prospers with the advantage of collaboration. To solve one or a set of challenging tasks, we can effectively interact with peers, fuse knowledge from different sources, continuously inspire, contribute, and develop the expertise for the benefit of the shared objectives. Human collaboration is flexible, adaptive, and scalable in terms of various cooperative constructions, collaborating across interdisciplinary, even seemingly unrelated domains, and building large-scale disciplined organizations for extremely complex tasks. On the other hand, while machine intelligence achieved tremendous success in the past decade, the ability to collaboratively solve complicated tasks is still limited compared to human intelligence.In this dissertation, we study the problem of collaborative learning - building flexible, generalizable, and scalable collaborative strategies to facilitate the efficiency of learning one or a set of objectives. Towards achieving this goal, we investigate the following concrete and fundamental problems:1. In the context of multi-task learning, can we enforce flexible forms of interactions from multiple tasks and adaptively incorporate human expert knowledge to guide the collaboration?2. In reinforcement learning, can we design collaborative methods that effectivelycollaborate among heterogeneous learning agents to improve the sample-efficiency?3. In multi-agent learning, can we develop a scalable collaborative strategy to coordinate a massive number of learning agents accomplishing a shared task?4. In federated learning, can we have provable benefit from increasing the number of collaborative learning agents?This thesis provides the first line of research to view the above learning fields in a unified framework, which includes novel algorithms for flexible, adaptive collaboration, real-world applications using scalable collaborative learning solutions, and fundamental theories for propelling the understanding of collaborative learning.
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