CAREER: A Sequential Learning Framework with Applications to Learning from Crowds

虽然传统的机器学习通常处理给定的静态数据，但许多在线数据是通过与人群标签员或客户等代理的一系列交互来收集的。该项目的激励性应用包括群体标签任务(这是利用人类智慧收集数据标签的强大范例)、顺序产品推荐和在线多产品定价。对于所有这些应用，在线学习和顺序决策是缺一不可的。这个项目的目标是开发新的序列学习算法，并提供严格的理论保证。所开发的框架不仅将做出基础性的技术贡献，还将为许多重要应用提供便利。例如，它将以显著降低的成本极大地改善来自人群标签者的聚合答案。它可以通过提供准确的推荐来提高业务收入的同时提高客户的满意度。此外，该项目还促进了面向商学院学生的机器学习新课程的开发，这有助于将数据科学的知识带给未来的商业领袖，并为K-12学生提供培训，重点是那些来自代表性不足的群体。该项目致力于开发一个统一的学习和决策框架，作为连接机器学习、随机优化和决策理论的智力桥梁。特别是，有三个相辅相成的研究方向。第一个推力创建了一套处理复杂任务结构的高效算法，例如在非平稳环境中使用传递性结构进行排序或使用组合结构进行产品推荐。该算法将具有有限个独立手臂的强盗学习扩展到潜在无限个手臂之间具有复杂关联结构的环境中。第二个推力寻求一种成本效益的范例，既可以在一定的预算约束下结合“最优停止”规则，也可以最小化样本的复杂性。第三个重点系统地评估了针对来自众包和其他与业务相关的应用的实际问题的算法和理论。此外，由于计算效率和可扩展性是一个重要的关注点，该项目还将推进分布式统计学习和随机优化领域。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

While traditional machine learning usually deals with given static data, many online data are collected via a sequence of interactions with agents such as crowd labelers or customers. The motivating applications of the project include crowd labeling tasks (which is a powerful paradigm for utilizing human wisdom to collect data labels), sequential product recommendation, and online multi-product pricing. For all these applications, online learning and sequential decision-making are indispensable to each other. The objective of this project is to develop new sequential learning algorithms with rigorous theoretical guarantees. The developed framework will not only make fundamental technical contributions but also facilitate many important applications. For example, it will greatly improve the aggregated answers from crowd labelers with a significantly reduced cost. It can enhance the revenue of business while improving the customers' satisfaction by providing accurate recommendations. In addition, this project also facilitates the development of new courses on machine learning for business school students, which helps bring the knowledge from data science to future business leaders, and provides training to K-12 students, with an emphasis on those from underrepresented groups.This project strives to develop a unified learning and decision-making framework, which serves as an intellectual bridge connecting machine learning, stochastic optimization, and decision theory. In particular, there are three complementary research thrusts. The first thrust creates a suite of efficient algorithms that deal with complex task structures, such as ranking with transitivity structures or product recommendation with combinatorial structures, in a non-stationary environment. The algorithms will extend the bandit learning with finite independent arms into the setting with a complex correlation structure among potentially infinite number of arms. The second thrust seeks a cost-effective paradigm that either incorporates "optimal stopping" rule under a certain budget constraint or minimizes the sample complexity. The third thrust systematically evaluates the algorithms and theories on real problems coming from both crowdsourcing and other business-related applications. Moreover, since the computational efficiency and scalability is an important focus, the project will also advance the distributed statistical learning and stochastic optimization fields.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.