发表机构
University of Zurich（苏黎世大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
IXPLORE提出一种结合预测拟合与稀疏感知似然的有界理想点估计算法，在五个基准数据集上优于现有方法，并通过网格后验推断量化不确定性。
AI 中文摘要
理想点估计被广泛用于分析和可视化政治数据。然而，选择相应的空间模型涉及多种权衡：虽然基于模型的方法（如项目反应理论（IRT））基于效用函数而非针对预测精度进行优化，但大多数机器学习（ML）替代方法在嵌入稀疏测试响应时难以泛化到训练数据之外。我们提出IXPLORE，一种有界理想点估计算法，将预测拟合目标与稀疏感知似然函数相结合。在涵盖调查、唱名表决和审议的五个基准数据集上，该方法在重构和插补误差方面超越了基于模型和基于ML的算法，尤其是对于响应稀疏的用户。此外，我们表明非线性特征变换可以进一步降低重构误差，同时保持视觉可解释性。为了量化不确定性，IXPLORE在有界二维潜在空间上应用基于网格的后验推断。作为PyPI上的Python包提供，IXPLORE为构建有界、可解释的政治地图提供了灵活框架，具有快速推断和强插补性能。
英文摘要
Ideal point estimation is widely used to analyze and visualize political data. However, selecting the corresponding spatial model involves various trade-offs: while model-based approaches such as Item Response Theory (IRT) are based on utility functions rather than optimized for predictive accuracy, most Machine Learning (ML) alternatives struggle to generalize beyond training data when embedding sparse test responses. We introduce IXPLORE, a bounded ideal point estimation algorithm that combines a predictive fit objective with a sparsity-aware likelihood function. On five benchmark datasets spanning surveys, roll calls, and deliberation, this approach surpasses model-based and ML-based algorithms on reconstruction and imputation error - especially for users with sparse responses. Furthermore, we show that non-linear feature transforms can further reduce the reconstruction error while remaining visually interpretable. To quantify uncertainty, IXPLORE applies grid-based posterior inference on a bounded 2D latent space. Available as a Python package on PyPI, IXPLORE offers a flexible framework for constructing bounded, interpretable political maps with fast inference and strong imputation performance.
