IXPLORE: Bounded Ideal Point Estimation with Grid-Based Uncertainty Quantification
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 a...
| Authors | Fynn Bachmann |
|---|---|
| Primary category | cs.LG |
| Published | 5 September 2026 |
| Last updated at source | 5 September 2026 |
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