面向有限不平衡数据组织的辅助学习
Assisted Learning for Organizations with Limited Imbalanced Data
AI总结：
针对数据有限且不平衡、又受共享政策限制的组织，提出辅助学习框架及面向深度学习与强化学习的随机训练算法，仅需偶尔通信即可获得近集中式训练性能。
摘要：
在大数据时代，许多大型组织正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到可获得的有限且不平衡数据的限制。在本工作中，我们开发了一个辅助学习框架，用于帮助各组织提升学习性能。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约束。其有限的不平衡数据常常导致有偏推断和次优决策。在辅助学习中，一个组织学习者从外部服务提供商处购买辅助服务，并力求仅在少数几轮辅助内增强其模型性能。我们为辅助深度学习和辅助强化学习均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，却仍能获得一个达到近乎先知性能的模型，就好像所有数据都被集中起来一样。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
