使用增量怀疑高斯过程进行野外学习
Learning in the Wild with Incremental Skeptical Gaussian Processes
摘要：
从人类监督中学习的能力是个人助理和其他人工智能交互应用的基础。在野外部署交互式学习器的两个主要挑战是监督的不可靠性和预测任务的复杂性。我们解决了一个简单但有代表性的设置，增量分类在野外，监督是嘈杂的，类的数量随着时间的推移而增长。为了解决这个问题，我们提出了一个以高斯过程（GPs）为中心的怀疑学习的重新设计。怀疑学习是最近的一种交互式策略，如果机器足够确信一个例子被错误标记，它会要求注释者重新考虑她的反馈。在许多情况下，这往往足以获得干净的监督。我们的重新设计，被称为ISGP，利用GP提供的不确定性估计，以更好地分配标签和矛盾查询，特别是在存在噪声的情况下。我们对合成数据和真实数据的实验表明，虽然怀疑学习的原始公式会产生过度自信的模型，但在野外可能会完全失败，ISGP在不同的噪声水平下以及观察到新的类时都能很好地工作。
英文摘要：
The ability to learn from human supervision is fundamental for personal assistants and other interactive applications of AI. Two central challenges for deploying interactive learners in the wild are the unreliable nature of the supervision and the varying complexity of the prediction task. We address a simple but representative setting, incremental classification in the wild, where the supervision is noisy and the number of classes grows over time. In order to tackle this task, we propose a redesign of skeptical learning centered around Gaussian Processes (GPs). Skeptical learning is a recent interactive strategy in which, if the machine is sufficiently confident that an example is mislabeled, it asks the annotator to reconsider her feedback. In many cases, this is often enough to obtain clean supervision. Our redesign, dubbed ISGP, leverages the uncertainty estimates supplied by GPs to better allocate labeling and contradiction queries, especially in the presence of noise. Our experiments on synthetic and real-world data show that, as a result, while the original formulation of skeptical learning produces over-confident models that can fail completely in the wild, ISGP works well at varying levels of noise and as new classes are observed.
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。
