Collaborative Research: RI: Small: Foundations of Few-Round Active Learning

监督式机器学习已经得到了广泛的应用，通常可以实现最先进的性能。然而，这些算法依赖于标记的训练实例，这可能很难获得。标记的实例通常是由人类完成的，需要时间和金钱来获得。主动学习通过识别最有信息的注释实例来最小化标注成本。虽然主动学习技术在产生具有更少标签的高性能模型方面显示出了希望，但由于需要与注释器进行多次交互，它们的应用仍然受到限制，这可能是耗时的或不可行的。该项目旨在推进主动学习算法，并了解其在有限交互回合情况下的基本能力。广泛的机器学习应用有望从这项研究的结果中受益，减少与获得足够数据以训练准确模型相关的时间和成本。此外，该项目通过实践研究和学习活动吸引代表性不足的少数民族学生，开发资源高效机器学习课程模块，并通过广泛的在线主动学习教程将我们的发现传播给工业界和学术界。该项目将对几轮主动学习进行全面调查，学习者可以在有限的几轮中主动请求对特定数据点的反馈。为了实现这一目标，该项目将交叉进行两项算法任务：稳健的数据效用量化和有限适应性的规划。首先，研究人员将探索测量未标记数据效用的方法，考虑到数据大小、潜在数据特征和下游学习任务。随后，该团队将开发算法，优化数据效用指标，同时在几轮主动学习设置中随着时间的推移提高指标的质量。该项目的研究结果将建立原则性的方法，以解决特定于几轮主动学习的新型探索-利用困境，并提供对适应性在预算学习中的作用的基本理解。最后，该项目将评估各种高影响力机器学习应用的拟议方法，包括自动驾驶、智能建筑、对话系统和生化工程。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Supervised machine learning has found widespread application, often achieving state-of-the-art performance. However, these algorithms rely on labeled training instances, which can be challenging to acquire. Labeled instances are often done by humans and require time and money to obtain. Active Learning strives to minimize labeling costs by identifying the most informative instances for annotation. While Active Learning techniques have shown promise in producing high-performance models with fewer labels, their applications remain constrained due to the necessity for multiple interaction rounds with annotators, which can be time-consuming or infeasible. This project aims to advance Active Learning algorithms and understanding of their fundamental capabilities in scenarios with limited interaction rounds. A broad spectrum of machine learning applications is expected to benefit from the results of this research, reducing the time and cost associated with obtaining sufficient data for training accurate models. Additionally, this project engages underrepresented minority students through hands-on research and learning activities, develops course modules on resource-efficient machine learning, and disseminates our findings to industry and academia via an extensive online Active Learning tutorial.This project will launch a comprehensive investigation of few-round active learning, where the learner can actively request feedback on specific data points within a limited number of rounds. To achieve this, the project will interleave two algorithmic tasks: robust data utility quantification and planning with limited adaptivity. First, the investigators will explore methods to measure the utility of unlabeled data, taking into account data size, underlying data characteristics, and downstream learning tasks. Subsequently, the team will develop algorithms that optimize the data utility metric while simultaneously improving the metric's quality over time in a few-round active learning setting. The project findings will establish principled approaches for addressing a novel exploration-exploitation dilemma specific to few-round active learning and provide a fundamental understanding of adaptivity's role in budgeted learning. Finally, the project will evaluate the proposed approaches across various high-impact machine learning applications, including autonomous driving, smart buildings, dialog systems, and biochemical engineering.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.