论文标题
主动标签：流随机梯度
Active Labeling: Streaming Stochastic Gradients
论文作者
论文摘要
机器学习的主力是随机梯度下降。要访问随机梯度，通常考虑训练数据集的迭代输入/输出对。有趣的是，看来人们不需要全面监督来访问随机梯度，这是本文的主要动机。在将“主动标签”问题形式化后，重点是通过部分监督进行主动学习之后，我们提供了一种流媒体技术，可证明可以最大程度地减少概括误差比样本数量的比率。我们深入说明了我们的技术以进行稳健的回归。
The workhorse of machine learning is stochastic gradient descent. To access stochastic gradients, it is common to consider iteratively input/output pairs of a training dataset. Interestingly, it appears that one does not need full supervision to access stochastic gradients, which is the main motivation of this paper. After formalizing the "active labeling" problem, which focuses on active learning with partial supervision, we provide a streaming technique that provably minimizes the ratio of generalization error over the number of samples. We illustrate our technique in depth for robust regression.
