EAGER: Toward Interpretation of Pairwise Learning

EAGER：对配对学习的解释

• 批准号: 1938167
• 负责人: Aidong Zhang
• 金额: $ 30万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1938167&HistoricalAwards=false
• 关键词: EAGER; Toward; Interpretation; Pairwise; Learning

To exhibit machine intelligence, it is critical for the system to not only make intelligent decisions, but also be able to explain how it arrives at those decisions. Explainable machine learning is an emerging research area aiming to develop new or modified machine learning techniques that will produce more explainable models. This project will develop novel methods to interpret the predictions of a class of learning models known as "pairwise learning models," which predicts relationships between instances rather than specific properties of an individual instance. For example, a consumer might want to know why the system recommends product A as similar to product B. The results of this research may benefit many real-world applications that are involved in pairwise learning, such as face recognition, visual tracking, information retrieval and bioinformatics. The development of the proposed approaches will contribute to the exploration of explainable machine learning as well as machine learning in general. Two exploratory research tasks are carried out to generate explanations on both individual predictions (local interpretations) and the entire model behaviors (global interpretation). The proposed local interpretation method adapts the concept of Shapley-value to explain prediction decisions about an arbitrary pair of input instances. The proposed global explanation method is a Bayesian non-parametric pairwise interpretation method with the elastic nets, which will explain feature importance across a population. Such global interpretability can facilitate the understanding of the sensitivity levels of a target pairwise learning model to specific input dimensions.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.

为了展示机器智能，系统不仅要做出智能决策，而且要能够解释它是如何做出这些决策的，这一点至关重要。可解释机器学习是一个新兴的研究领域，旨在开发新的或改进的机器学习技术，以产生更多的可解释模型。该项目将开发新的方法来解释一类被称为“成对学习模型”的学习模型的预测，这种模型预测实例之间的关系，而不是单个实例的特定属性。例如，消费者可能想知道为什么系统推荐产品a与产品b相似。这项研究的结果可能有益于许多涉及成对学习的现实应用，如面部识别、视觉跟踪、信息检索和生物信息学。所提出的方法的发展将有助于探索可解释的机器学习以及一般的机器学习。进行了两个探索性研究任务，以产生对个体预测（局部解释）和整个模型行为（全局解释）的解释。提出的局部解释方法采用shapley值的概念来解释任意一对输入实例的预测决策。提出的全局解释方法是一种贝叶斯非参数配对解释方法，该方法具有弹性网，可以解释整个种群的特征重要性。这种全局可解释性有助于理解目标两两学习模型对特定输入维度的敏感性水平。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。

项目成果

Towards Automating Model Explanations with Certified Robustness Guarantees

• DOI: 10.1609/aaai.v36i6.20651
• 发表时间: 2022-06
• 期刊: ArXiv
• 作者: Mengdi Huai;Jinduo Liu;Chenglin Miao;Liuyi Yao;Aidong Zhang