Learning Nonparametric Choice Models with Discrete Fourier Analysis
提出用离散傅里叶分析来估计非参数选择模型，无需明确模型描述，只需少量数据查询即可高精度逼近任意选择函数，比常用启发式方法误差更小。
Nonparametric choice models offer broad applicability and robustness. However, the exponentially large parameter space leads practitioners to use heuristics for estimation. We introduce an alternative approach to modeling and estimating nonparametric choice models using discrete Fourier analysis. We demonstrate that any choice function can be approximated with a small number of Fourier parameters. Our sample-efficient, active-learning algorithms, without requiring an explicit model description, need at most [Formula: see text] data queries to estimate any choice function up to [Formula: see text] accuracy. Computational studies show significant error reduction with Fourier methods compared with common heuristics for nonparametric choice estimation in both simulated and real data. Funding: Haoyu Song received financial support from the National Science Foundation [Grant CCF-2128702].