Shapley values are among the most popular tools for explaining predictions of
blackbox machine learning models. However, their high computational cost
motivates the use of sampling approximations, inducing a considerable degree of
uncertainty. To stabilize these model explanations, we propose ControlSHAP, an
approach based on the Monte Carlo technique of control variates. Our
methodology is applicable to any machine learning model and requires virtually
no extra computation or modeling effort. On several high-dimensional datasets,
we find it can produce dramatic reductions in the Monte Carlo variability of
Shapley estimates.

使用 Monte Carlo 控制变量技术的 ControlSHAP 方法可以有效减少 Shapley 估计的 Monte Carlo 变异性。

利用控制变量稳定 Shapley 值的估计

Stabilizing Estimates of Shapley Values with Control Variates

We present a neural modeling framework for Non-Line-of-Sight (NLOS) imaging.
Previous solutions have sought to explicitly recover the 3D geometry (e.g., as
point clouds) or voxel density (e.g., within a pre-defined volume) of the
hidden scene. In contrast, inspired by the recent Neural Radiance Field (NeRF)
approach, we use a multi-layer perceptron (MLP) to represent the neural
transient field or NeTF. However, NeTF measures the transient over spherical
wavefronts rather than the radiance along lines. We therefore formulate a
spherical volume NeTF reconstruction pipeline, applicable to both confocal and
non-confocal setups. Compared with NeRF, NeTF samples a much sparser set of
viewpoints (scanning spots) and the sampling is highly uneven. We thus
introduce a Monte Carlo technique to improve the robustness in the
reconstruction. Comprehensive experiments on synthetic and real datasets
demonstrate NeTF provides higher quality reconstruction and preserves fine
details largely missing in the state-of-the-art.

提出了一种基于神经建模的非直视成像框架，采用多层感知器（MLP）来表示神经瞬态场（NeTF），并引入 Monte Carlo 技术来提高重建的鲁棒性和细节保留。实验结果表明，该方法在合成和实际数据集上提供了更高质量的重建并保留了先前方法中缺失的细节。