Collaborative Research: RI: Medium: Submodular Information Functions with Applications to Machine Learning

合作研究：RI：中：子模信息函数及其在机器学习中的应用

基本信息

批准号：
2106389
负责人：
Jeffrey Bilmes
金额：
$ 60万
依托单位：
University of Washington
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-15 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106389&HistoricalAwards=false
关键词：
Collaborative Research RI Medium Submodular

项目摘要

A growing number of machine learning applications involve selecting subsets of data. Examples include selecting smaller subsets from a much larger dataset to label (to save labeling costs) and to train (to reduce computational costs), or selecting a summary of a video or a photo collection to ease viewing by a person. Submodularity is a natural way to address these problems because it naturally models many aspects like diversity, representation, and coverage. In this project, the PIs will study a rich class of submodular information measures that model not only diversity, representation, coverage but also constructs such as relevance and irrelevance to certain target concepts. One application of this is selecting a data summary with certain user specifications -- e.g., a summary relevant to a given query or under a privacy constraint (a photo summary relevant to a specific person or one which avoids certain personal information). Another application is to interactively select data samples to label in the presence of rare classes or while avoiding outliers (e.g., cancerous images as rare classes for medical imaging tasks). Advances in this field can have implications in many areas including data summarization, reducing labeling efforts (in tasks like medical imaging), and reducing the carbon footprint for training deep learning models on massive datasets.The underlying mathematical model proposed in this project is a rich class of functions called ``submodular information measures``, which includes submodular mutual information, submodular conditional gain, submodular multi-set mutual information, directed submodular mutual information, and combinatorial independence. Specifically, the PIs will investigate and develop: (1) rich theoretical properties and instantiations of these submodular information measures; (2) optimization algorithms, approximation bounds, and hardness results of the associated optimization problems; (3) applications of the submodular information measures in data summarization, data subset selection, active learning, clustering, and diversified partitioning. While pursuing these activities, the PIs will involve undergraduate and under-represented high-school students in this research to inspire them to pursue careers in AI/ML and other STEM-related 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.

越来越多的机器学习应用程序涉及选择数据子集。示例包括从更大的数据集中选择较小的子集进行标记（以节省标记成本）和训练（以减少计算成本），或者选择视频或照片集的摘要以方便人们查看。子模块化是解决这些问题的一种自然方式，因为它自然地模拟了许多方面，例如多样性、代表性和覆盖范围。在这个项目中，PI 将研究丰富的子模块信息度量，这些度量不仅对多样性、代表性、覆盖范围进行建模，而且还对诸如与某些目标概念的相关性和不相关性等结构进行建模。其一种应用是选择具有某些用户规范的数据摘要——例如，与给定查询相关或在隐私约束下的摘要（与特定人相关的照片摘要或避免某些个人信息的照片摘要）。另一个应用是在存在稀有类别或避免异常值的情况下以交互方式选择要标记的数据样本（例如，将癌症图像作为医学成像任务的稀有类别）。该领域的进步可能会对许多领域产生影响，包括数据汇总、减少标记工作（在医学成像等任务中）以及减少在海量数据集上训练深度学习模型的碳足迹。该项目提出的基础数学模型是丰富的称为“子模信息度量”的函数类，包括子模互信息、子模条件增益、子模多集互信息、有向子模互信息和组合独立性。具体而言，PI 将研究和开发：（1）这些子模块信息度量的丰富理论属性和实例； (2) 相关优化问题的优化算法、近似界限和硬度结果； (3)子模信息测度在数据汇总、数据子集选择、主动学习、聚类和多样化划分中的应用。在开展这些活动的同时，PI 将让本科生和代表性不足的高中生参与这项研究，以激励他们追求 AI/ML 和其他 STEM 相关领域的职业。该奖项反映了 NSF 的法定使命，并被认为是值得的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来提供支持。