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.

虽然机器学习在不同领域的影响力不断增强——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一些局限性代表了其安全和负责任的部署的重要缺陷。这些限制之一是这些预测变量缺乏可解释性，使得难以忠实地确定给定输入的最相关部分在产生特定输出中的作用。另一个限制是它们的脆弱性，因为即使输入的扰动非常小，这些输出也可能非常不稳定。这些问题可能会影响现代机器学习工具在敏感领域（例如医学成像）的安全部署。该项目将开发正式的方法和算法来缓解这些缺点。该职业项目将开发一个通用框架，以稳健且可验证的方式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。这些定义将根据局部条件独立性测试给出，同时对预测函数做出最小的假设，并对语义上重要的概念进行扩展。其次，该项目将提出并分析算法，以证明流形上局部预测模型的稳定性，并保证模型解释的稳定性和鲁棒性。该项目得出的方法将针对一系列医学成像问题进行评估，包括胸部 X 光检查和计算机断层扫描。此外，该项目还将开展全面的教育和外展计划，致力于提高少数族裔学生在 STEM 中的代表性，包括通过约翰霍普金斯大学教育外展中心进行 K-12 和社区外展、战略性本科生和研究生研究项目以及广泛的项目。除其他举措外，还向科学界和公众传播。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。