In the era of big data, many big organizations are integrating machine learning into their work pipelines to facilitate data analysis. However, the performance of their trained models is often restricted by limited and imbalanced data available to them. In this work, we develop an assisted learning framework for assisting organizations to improve their learning performance. The organizations have sufficient computation resources but are subject to stringent data-sharing and collaboration policies. Their limited imbalanced data often cause biased inference and sub-optimal decision-making. In assisted learning, an organizational learner purchases assistance service from an external service provider and aims to enhance its model performance within only a few assistance rounds. We develop effective stochastic training algorithms for both 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, but still obtain a model that achieves near-oracle performance as if all the data were centralized.

翻译：在大数据时代，许多大型组织正在将机器学习整合到其工作流程中以促进数据分析。然而，其训练模型的性能往往受限于可用的有限且不平衡的数据。在本研究中，我们开发了一个辅助学习框架，用于协助组织提升其学习性能。这些组织拥有充足的计算资源，但受到严格的数据共享与协作政策的约束。其有限且不平衡的数据常常导致有偏的推断和次优的决策。在辅助学习中，组织学习者从外部服务提供商购买辅助服务，并旨在仅通过少量辅助轮次来提升其模型性能。我们为辅助深度学习与辅助强化学习分别开发了有效的随机训练算法。与需要频繁传输梯度或模型的现有分布式算法不同，我们的框架允许学习者仅偶尔与服务提供商共享信息，但仍能获得一个性能接近如同所有数据集中处理时的近乎最优模型。