论文标题
预先训练的分类器的在线主动模型选择
Online Active Model Selection for Pre-trained Classifiers
论文作者
论文摘要
给定$ K $预训练的分类器和一系列未标记的数据示例，我们如何积极决定何时查询标签，以便我们可以在少量查询的同时将最佳模型与其他模型区分开来？回答这个问题对各种实际情况都有深远的影响。在这项工作中，我们设计了一种在线选择性抽样方法，该方法在任何一轮中都可以积极选择信息示例，并以高概率为最佳模型。我们的算法可用于对抗和随机流的在线预测任务。我们为我们的算法建立了几种理论保证，并在我们的实验研究中广泛证明了其有效性。
Given $k$ pre-trained classifiers and a stream of unlabeled data examples, how can we actively decide when to query a label so that we can distinguish the best model from the rest while making a small number of queries? Answering this question has a profound impact on a range of practical scenarios. In this work, we design an online selective sampling approach that actively selects informative examples to label and outputs the best model with high probability at any round. Our algorithm can be used for online prediction tasks for both adversarial and stochastic streams. We establish several theoretical guarantees for our algorithm and extensively demonstrate its effectiveness in our experimental studies.
