时变倾向得分：弥合过去与现在之间的差距
Time-Varying Propensity Score to Bridge the Gap between the Past and Present
AI总结：
本文提出时变倾向得分方法，通过检测数据分布的渐进偏移并选择性采样过去以相似方式演变的数据来更新模型，在监督学习和强化学习任务中验证了其有效性。
摘要：
机器学习模型在真实世界中的部署具有挑战性，因为数据会随时间演变。虽然当数据以任意方式演变时，没有任何模型能够正常工作，但如果这些变化存在某种模式，我们或许能够设计方法来解决这一问题。本文针对数据逐渐演变的情形展开研究。我们引入了一种时变倾向得分，它能够检测数据分布中的渐进偏移，从而使我们能够有选择地采样过去的数据来更新模型——不仅仅是像标准倾向得分那样采样过去相似的数据，还包括过去以相似方式演变的数据。时变倾向得分具有相当的通用性：我们展示了实现它的不同方式，并在各种问题上对其进行了评估，这些问题涵盖监督学习（例如图像分类问题，其中数据经历一系列渐进偏移）到强化学习任务（例如机器人操作和连续控制，其中数据随着策略或任务的变化而发生偏移）。
英文摘要：
Real-world deployment of machine learning models is challenging because data evolves over time. While no model can work when data evolves in an arbitrary fashion, if there is some pattern to these changes, we might be able to design methods to address it. This paper addresses situations when data evolves gradually. We introduce a time-varying propensity score that can detect gradual shifts in the distribution of data which allows us to selectively sample past data to update the model -- not just similar data from the past like that of a standard propensity score but also data that evolved in a similar fashion in the past. The time-varying propensity score is quite general: we demonstrate different ways of implementing it and evaluate it on a variety of problems ranging from supervised learning (e.g., image classification problems) where data undergoes a sequence of gradual shifts, to reinforcement learning tasks (e.g., robotic manipulation and continuous control) where data shifts as the policy or the task changes.
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。
