界定材料机器遗忘中的再训练等价性与删除下限
Bounding Retraining Equivalence and the Deletion Floor in Materials Machine Unlearning
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中文总结 AI 辅助
本研究针对材料机器学习中机器遗忘的模糊性，定义删除下限并推导理论界，实验表明删除后保留相关样本可大幅降低再训练损失，并建议综合报告多项指标以评估遗忘效果。
中文摘要 AI 辅助
在材料机器学习中，紧密相关的保留结构即使在移除特定记录后仍能维持准确的属性预测，这使得删除后的预测误差成为机器遗忘的一个模糊指标。为解决这一模糊性，我们将删除下限定义为在删除请求下，按指定再训练程序所得到的期望目标损失。标准不可区分性约束产生了一个尖锐区间，用于界定更新目标损失围绕该基线参考值的范围。理论上，条件邻居界将低删除下限直接与保留拟合、预测正则性及局部标签一致性联系起来，而精确的岭回归恒等式则将残差拟合与记录删除引起的预测变化分离开来。实验上，受控冗余扫描显示，当删除后保留一个相关样本时，中位归一化再训练损失下降约8倍。在配对材料项目研究中的两种不同拟合机制下，下限机制在超过50%的共享请求上也表现出更大的预测变化。与近似更新及原始模型的系统比较，将故意的目标抑制与保留的整体模型效用解耦。因此，请求级遗忘评估应同时报告参考损失、预测变化和保留效用，并针对再训练本身所遗留的内容来解读删除后的准确性。
英文摘要
In materials machine learning, closely related retained structures can sustain accurate property predictions even after removing a specific record, rendering post-deletion prediction error an ambiguous metric for machine unlearning. To resolve this ambiguity, we define the deletion floor as the expected target loss under a specified retraining procedure at the deleted request. Standard indistinguishability constraints yield a sharp interval bounding an update's target loss around this baseline reference. Theoretically, a conditional neighbor bound links a low deletion floor directly to retained fit, prediction regularity, and local label agreement, while an exact ridge identity isolates residual fit from the prediction change induced by record deletion. Empirically, controlled redundancy sweeps show an $\approx 8\times$ drop in median normalized retraining loss when one retained relative remains after deletion. Across two distinct fitting regimes in a paired Materials Project study, the lower-floor regime also exhibits a larger prediction change on more than 50% of the shared requests. Systematic comparisons against approximate updates and the original model decouple deliberate target suppression from preserved overall model utility. Consequently, request-level unlearning evaluations should report reference loss, prediction change, and retained utility together, interpreting post-deletion accuracy against what retraining itself leaves behind.
