We develop an assisted learning framework for assisting organization-level learners to improve their learning performance with limited and imbalanced data. In particular, learners at the organization level usually have sufficient computation resource, but are subject to stringent collaboration policy and information privacy. Their limited imbalanced data often cause biased inference and sub-optimal decision-making. In our assisted learning framework, an organizational learner purchases assistance service from a service provider and aims to enhance its model performance within a few assistance rounds. We develop effective stochastic training algorithms for assisted deep learning and assisted reinforcement learning. Different from existing distributed algorithms that need to frequently transmit gradients or models, our framework allows the learner to only occasionally share information with the service provider, and still achieve a near-oracle model as if all the data were centralized.

翻译：我们开发了一个协助学习框架,帮助组织一级的学习者利用有限和不平衡的数据提高学习成绩;特别是,组织一级的学习者通常有足够的计算资源,但需遵守严格的合作政策和信息隐私;他们有限的不平衡数据往往导致有偏向的推论和次优决策;在我们的援助学习框架内,一个组织学习者从一个服务提供者那里购买援助服务,目的是在几个援助回合中提高其示范性业绩;我们开发了有效的随机培训算法,用于协助深层学习和协助强化学习。与现有的分布式算法不同,现有分布式算法需要经常传输梯度或模型,我们的框架允许学习者偶尔与服务提供者分享信息,并且仍然实现近巧模式,就像所有数据都是集中的一样。