CAREER: Interpretable and Robust Machine Learning Models: Analysis and Algorithms

职业：可解释且稳健的机器学习模型：分析和算法

• 批准号: 2239787
• 负责人: Jeremias Sulam
• 金额: $ 57.29万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239787&HistoricalAwards=false
• 关键词: CAREER; Interpretable; Robust; Machine; Learning

While the impact of machine learning continues to increase in different areas---from recommendation systems to algorithmic trading, and from medical imaging diagnosis to molecular biology---some limitations of these increasingly complex models represent important shortcomings for their safe and responsible deployment. One of these limitations is the lack of interpretability of these predictors, making it difficult to faithfully determine the role of the most relevant portions of a given input in producing a certain output. Another limitation is their brittleness, as these outputs can also be remarkably unstable even to very small perturbations of the inputs. These problems can compromise the safe deployment of modern machine learning tools in sensitive domains, such as medical imaging. This project will develop formal methods and algorithms to alleviate these shortcomings.This CAREER project will develop a general framework to interpret complex predictors in a robust and certifiable manner. In particular, this project will first define new notions of local feature importance and develop correct and efficient methods to estimate them. These definitions will be given in terms of local conditional independence tests while making minimal assumptions about the prediction functions, as well as extensions to semantically important concepts. Second, this project will propose and analyze algorithms to certify the stability of predictive models locally on manifolds, as well as guarantee the stability and robustness of model interpretations. The methods derived from this project will be evaluated on a series of medical imaging problems that include chest X-rays and computed tomography. In addition, this project will carry out a holistic educational and outreach program dedicated to increasing the representation of minority students in STEM, including K-12 and community outreach through the Johns Hopkins Center for Educational Outreach, strategic undergraduate and graduate research projects, and broad dissemination to both the scientific community and the general public, among other initiatives.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.

尽管机器学习的影响在不同领域的影响不断增加 - 从推荐系统到算法交易，从医学成像诊断到分子生物学 - 这些日益复杂的模型的某些局限性代表了其安全和负责任的部署的重要缺点。这些局限性之一是这些预测因素缺乏解释性，因此很难忠实地确定给定输入中最相关部分在产生某个输出中的作用。另一个限制是它们的脆性，因为这些输出即使对输入非常小的扰动也可能非常不稳定。这些问题可能会损害现代机器学习工具在敏感领域（例如医学成像）中的安全部署。该项目将开发正式的方法和算法来减轻这些缺点。本职业项目将开发一个通用框架，以可靠且可证明的方式来解释复杂的预测变量。特别是，该项目将首先定义局部特征重要性的新概念，并开发正确有效的方法来估计它们。这些定义将根据当地有条件独立性测试给出，同时对预测函数进行最小化的假设以及对语义上重要概念的扩展。其次，该项目将提出和分析算法，以证明在歧管上本地预测模型的稳定性，并确保模型解释的稳定性和鲁棒性。从该项目中得出的方法将在包括胸部X射线和计算机断层扫描在内的一系列医学成像问题上进行评估。此外，该项目将通过约翰·霍普金斯教育外展，战略本科和研究生研究项目，包括K-12和社区宣传在内的少数族裔学生的代表，包括K-12和社区宣传活动，并进行整体教育和宣传计划，并通过纽约州的一般公众以及其他宣布的代表，包括对科学社区和其他奖项的广泛评估。基金会的智力优点和更广泛的影响评论标准。