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審計與修復離散選擇的表格基礎模型中的經濟有效性
Yingshuo Wan · 2026-05-27 · via cs.LG updates on arXiv.org

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摘要:表格型基礎模型在選擇預測任務上達到強勁的準確性,但它的預測經常違反了這些任務所要求的經濟邏輯:提高價格有時會增加預測的需求,而暗示的支付意願估計經常是負數或不切實際。我們提出了一個兩階段的調整器,將基礎模型的預測嵌入到效用最大化框架中。在第一階段,我們估計一個標準的選擇模型,其參數被約束必須遵守經濟理論。在第二階段,我們凍結這些參數並訓練一個校正項,它將基礎模型的預測作為額外的信息包含進去。結果是一個模型,它繼承了基礎模型的準確性提升,同時在政策干擾下保證單調的價格-需求關係,並產生可解析計算的折衷衡量。在兩個交通數據集上,調整器比標準的邏吉模型恢復了最高達13個百分點的準確性,同時保持完美的經濟一致性,這點既不是原始的基礎模型也無法通過傳統的蒸餾實現。
評論: 5 頁,1 個表格。被收錄於 FMSD 研討會,ICML 2026
主題: 機器學習 (cs.LG); 藝術智能 (cs.AI); 變量經濟學 (econ.EM)
引用格式: arXiv:2605.26559 [cs.LG]
  (或 arXiv:2605.26559v1 [cs.LG]) for this version)
  https://doi.org/10.48550/arXiv.2605.26559

arXiv發行的DOI透過DataCite(待註冊)

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From: Yingshuo Wang [查看郵件]
[v1] 周二,2026年5月26日 05:13:20 UTC (32 KB)