提出路径采样梯度，提升归因精度并降低噪声。
Path-Sampled Integrated Gradients
- 通过沿路径采样基线计算期望梯度，改进归因方法。
- 误差收敛速度从O(m⁻¹/²)提升至O(m⁻¹)，且方差降低1/3。
- 保留线性与不变性等关键性质，适合高噪声模型分析。
我们提出路径采样积分梯度（PS-IG），通过在线性插值路径上采样基线计算特征归因的期望值。理论上证明，当权重函数匹配采样密度的累积分布函数时，PS-IG与路径加权积分梯度等价。该等价性使随机期望可通过确定性黎曼和评估，将光滑模型下的误差收敛率从O(m⁻¹/²)提升至O(m⁻¹)。此外，我们从理论上证明，PS-IG作为降噪滤波器，可严格降低归因方差——在均匀采样下降低1/3，同时保持线性、实现不变性等核心公理性质。
原文摘要 · Abstract (English)
We introduce path-sampled integrated gradients (PS-IG), a framework that generalizes feature attribution by computing the expected value over baselines sampled along the linear interpolation path. We prove that PS-IG is mathematically equivalent to path-weighted integrated gradients, provided the weighting function matches the cumulative distribution function of the sampling density. This equivalence allows the stochastic expectation to be evaluated via a deterministic Riemann sum, improving the error convergence rate from $O(m^{-1/2})$ to $O(m^{-1})$ for smooth models. Furthermore, we demonstrate analytically that PS-IG functions as a variance-reducing filter against gradient noise - strictly lowering attribution variance by a factor of 1/3 under uniform sampling - while preserving key axiomatic properties such as linearity and implementation invariance.
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