Machine learning has achieved remarkable success across a wide range of applications, yet many of its most effective methods rely on access to large amounts of labeled data or extensive online interaction. In practice, acquiring high-quality labels and making decisions through trial-and-error can be expensive, time-consuming, or risky, particularly in large-scale or high-stakes settings. This dissertation studies interactive machine learning, in which the learner actively influences how information is collected or which actions are taken, using past observations to guide future interactions. We develop new algorithmic principles and establish fundamental limits for interactive learning along three dimensions: active learning with noisy data and rich model classes, sequential decision making with large action spaces, and model selection under partial feedback. Our results include the first computationally efficient active learning algorithms achieving exponential label savings without low-noise assumptions; the first efficient, general-purpose contextual bandit algorithms whose guarantees are independent of the size of the action space; and the first tight characterizations of the fundamental cost of model selection in sequential decision making. Overall, this dissertation advances the theoretical foundations of interactive learning by developing algorithms that are statistically optimal and computationally efficient, while also providing principled guidance for deploying interactive learning methods in large-scale, real-world settings.

翻译：机器学习已在众多应用领域取得显著成功，但其最有效的方法大多依赖于大量标注数据或广泛的在线交互。实践中，获取高质量标注数据以及通过试错进行决策往往成本高昂、耗时且存在风险，在大规模或高风险场景中尤为如此。本论文研究交互式机器学习，即学习器通过历史观测指导未来交互，主动影响信息收集方式或行动选择。我们围绕三个维度提出新的算法原理并建立交互式学习的基本理论边界：含噪声数据与丰富模型类的主动学习、大动作空间的序列决策，以及部分反馈下的模型选择。研究成果包括：首个无需低噪声假设即可实现指数级标注效率提升的计算高效主动学习算法；首个保证与动作空间规模无关的高效通用上下文赌博机算法；以及对序列决策中模型选择基本代价的首个紧致刻画。总体而言，本论文通过开发统计最优且计算高效的算法，推进了交互式学习的理论基础，同时为在大规模实际场景中部署交互式学习方法提供了理论指导。