DiCoFlex：具有灵活控制的模型无关多样化反事实生成方法
DiCoFlex: Model-agnostic diverse counterfactuals with flexible control
AI总结：
本文提出模型无关的条件生成框架DiCoFlex，利用条件归一化流在单次前向传播中生成多个多样化反事实，并支持推理时灵活定制稀疏性与可执行性等约束，在多个指标上优于现有方法。
摘要：
反事实解释在可解释人工智能（XAI）中发挥着关键作用，它通过提供直观、人类可理解的替代方案来阐明机器学习模型的决策。尽管具有重要意义，现有的反事实生成方法通常需要持续访问预测模型，针对每个实例进行计算密集型的优化，并且缺乏在不重新训练的情况下适应新的用户定义约束的灵活性。在本文中，我们提出了DiCoFlex，一种新颖的模型无关、条件生成框架，能够在单次前向传播中生成多个多样化的反事实。DiCoFlex利用仅在标注数据上训练的条件归一化流，通过在推理时实现用户驱动的约束（如稀疏性和可执行性）实时定制，解决了上述关键局限。在标准基准数据集上的大量实验表明，DiCoFlex在有效性、多样性、邻近性和约束遵循方面均优于现有方法，使其成为敏感决策领域中反事实生成的一种实用且可扩展的解决方案。
英文摘要：
Counterfactual explanations play a pivotal role in explainable artificial intelligence (XAI) by offering intuitive, human-understandable alternatives that elucidate machine learning model decisions. Despite their significance, existing methods for generating counterfactuals often require constant access to the predictive model, involve computationally intensive optimization for each instance and lack the flexibility to adapt to new user-defined constraints without retraining. In this paper, we propose DiCoFlex, a novel model-agnostic, conditional generative framework that produces multiple diverse counterfactuals in a single forward pass. Leveraging conditional normalizing flows trained solely on labeled data, DiCoFlex addresses key limitations by enabling real-time user-driven customization of constraints such as sparsity and actionability at inference time. Extensive experiments on standard benchmark datasets show that DiCoFlex outperforms existing methods in terms of validity, diversity, proximity, and constraint adherence, making it a practical and scalable solution for counterfactual generation in sensitive decision-making domains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
