EAGER: New Algorithms for Feature-Efficient Learning

这个探索性研究项目的主要目标是发明理论上合理和实用的机器学习算法，旨在在部署期间访问数据功能的各种限制下表现良好。 这解决了许多机器学习应用程序中遇到的一个主要困难：在运行算法时，访问数据的特征可能是耗时或昂贵的。 例如，在医学诊断中，患者的特征可能对应于医学测试的结果，这可能花费大量的时间来运行，带来巨大的成本，甚至带来健康风险。 当前的机器学习技术不足以解决此类障碍。 该项目涉及将功能高效优化纳入机器学习算法训练阶段的方法，以及创建新框架以降低错误率和与获取功能相关的成本。功能高效算法的成功开发为从医学诊断到万维网上的查询回答等应用领域创造了重要的进步。 该项目的其他方面包括将其研究成果纳入研究生课程，并扩大参与研究。该项目研究了联合优化特征成本，预测时间和分类错误率的新模型，以创建功能有效的预测器。 这个探索性项目的技术包括解决原始优化问题，创建新的机器学习约简，以及通过统计查询神谕分析问题。 这项工作的另一个方面是通过将功能-成本优化转移到预算提升分类器和支持向量机的训练阶段来解决预算学习形式化问题。该奖项反映了NSF的法定使命，并通过使用基金会的智力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 main goal of this exploratory research project is to invent theoretically sound and practical machine-learning algorithms designed to perform well under various limitations involving access to data features during deployment. This tackles a major difficulty encountered in many machine-learning applications: in running an algorithm, accessing features of the data can be time consuming or costly. For example, in medical diagnosis, features of patients may correspond to results of medical tests, which can take significant time to run, carry enormous cost, and even impose heath risks. Current machine-learning techniques are ill-equipped to tackle such impediments. This project involves approaches that incorporate feature-efficient optimization into the training phase of machine-learning algorithms and also the creation of new frameworks for reducing both error rates and costs associated with acquiring features. Successful developments in feature-efficient algorithms create an important advance for application areas ranging from medical diagnosis to query-answering on the World Wide Web. Additional facets of this project include incorporating its research findings into graduate courses and broadening participation in research.This project investigates new models for jointly optimizing feature costs, prediction time, and classification error rates, to create feature-efficient predictors. Techniques for this exploratory project include solving original optimization problems, creating novel machine-learning reductions, and analyzing the problem via statistical query oracles. Another aspect of this work is to tackle a budgeted learning formalization by moving the feature-cost optimization into the training phase of budgeted boosting classifiers and support vector machines.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.