Collaborative Research: CIF: Small: Learning from Multiple Biased Sources

人工智能领域，尤其是机器学习，关注的是通过从过去的任务中学习来自动化任务的执行。例子包括对图像进行分类和成功通过迷宫。经典的机器学习方法假设过去出现的任务，或“训练数据”，准确地代表任务的未来出现。然而，在许多应用中，训练数据是从多个来源提取的，这些来源反映了不同质量程度的未来事件。例子包括由众包用户标记的图像或随机模拟迷宫的导航。这个项目的目标是发展从多偏见来源学习的理论基础。这项工作将受到上述众包和自主导航以及视频监控和核威胁探测应用的推动。这项研究将支持密歇根大学和波士顿大学的博士和本科生的跨学科发展。为了实现这些目标，研究人员将为目前几乎没有理论存在的四大类机器学习问题建立理论基础：(1)来自多个损坏源的分类，(2)重叠非参数聚类的聚类，(3)Sim2Real强化学习和(4)零射击学习。这个项目的理论贡献将采取泛化误差界限、遗憾界限和样本复杂性界限的形式，同时也尽可能强调无分布或一般非参数模型。为了解决从多个来源提取和汇总有偏见信息的挑战，分析将开发新的技术工具，包括加权Rademacher复杂度，来自有偏见的强盗反馈的遗憾分析，以及用于密度估计的oracle不等式，这些工具可能会在其他学习环境中得到应用。这项工作所产生的研究将突出从多个来源学习的独特特征，包括与多个样本量相关的各种问题。更一般地说，该研究开发了在批处理和顺序学习设置以及各种源间依赖模型下集成异构数据源的原则方法。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。

The field of artificial intelligence, and especially machine learning, is concerned with automating the performance of a task by learning from past performances of that task. Examples include classifying images and successfully navigating a maze. Classical machine learning methods assume that past occurrences of a task, or “training data,” accurately represent future occurrences of the task. In many applications, however, training data are drawn from multiple sources that reflect future occurrences with varying degrees of quality. Examples include images labeled by crowd-sourced users or navigation of randomly simulated mazes. The objective of this project is to develop theoretical foundations of learning from multiple biased sources. The work will be motivated by applications in crowdsourcing and autonomous navigation as described above, as well as in video surveillance and nuclear threat detection. This research will support the cross-disciplinary development of a diverse cohort of PhD and undergraduate students at the University of Michigan and at Boston University.To achieve these goals, the investigators will establish theoretical foundations for four broad classes of machine learning problems for which virtually no theory presently exists: (1) Classification from multiple corrupted sources, (2) Clustering with overlapping, nonparametric clusters, (3) Sim2Real reinforcement learning, and (4) Zero-shot learning. This project's theoretical contributions will take the form of generalization error bounds, regret bounds, and sample complexity bounds, while also emphasizing distribution free or general nonparametric models wherever possible. To address the challenges of eliciting and aggregating biased information from multiple sources, the analyses will develop new technical tools, including weighted Rademacher complexity, regret analysis from biased bandit feedback, and oracle inequalities for density estimation, that are likely to find application in other learning settings. The research resulting from this effort will highlight distinctive features of learning from multiple sources, including various questions associated with multiple sample sizes. More generally, the research develops principled approaches for integrating heterogeneous data sources in both batch and sequential learning settings and under a variety of inter-source dependence models.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.