Attribution methods shed light on the explainability of data-driven
approaches such as deep learning models by revealing the most contributing
features to decisions that have been made. A widely accepted way of deriving
feature attributions is to analyze the gradients of the target function with
respect to input features. Analysis of gradients requires full access to the
target system, meaning that solutions of this kind treat the target system as a
white-box. However, the white-box assumption may be untenable due to security
and safety concerns, thus limiting their practical applications. As an answer
to the limited flexibility, this paper presents GEEX (gradient-estimation-based
explanation), an explanation method that delivers gradient-like explanations
under a black-box setting. Furthermore, we integrate the proposed method with a
path method. The resulting approach iGEEX (integrated GEEX) satisfies the four
fundamental axioms of attribution methods: sensitivity, insensitivity,
implementation invariance, and linearity. With a focus on image data, the
exhaustive experiments empirically show that the proposed methods outperform
state-of-the-art black-box methods and achieve competitive performance compared
to the ones with full access.

本文介绍了一种以黑盒为基础的梯度估计解释方法，称为 GEEX，通过分析梯度来揭示数据驱动方法（如深度学习模型）中做出决策的最重要特征，并将其与路径方法相结合，得到了用于图像数据的 iGEEX 方法，证明了该方法在黑盒场景中优于现有方法，并有着与完全访问的方法相媲美的性能。

黑盒环境下类梯度解释：当黑盒解释变得如白盒一样好

On Gradient-like Explanation under a Black-box Setting: When Black-box  Explanations Become as Good as White-box

Optimizing functions without access to gradients is the remit of black-box
methods such as evolution strategies. While highly general, their learning
dynamics are often times heuristic and inflexible - exactly the limitations
that meta-learning can address. Hence, we propose to discover effective update
rules for evolution strategies via meta-learning. Concretely, our approach
employs a search strategy parametrized by a self-attention-based architecture,
which guarantees the update rule is invariant to the ordering of the candidate
solutions. We show that meta-evolving this system on a small set of
representative low-dimensional analytic optimization problems is sufficient to
discover new evolution strategies capable of generalizing to unseen
optimization problems, population sizes and optimization horizons. Furthermore,
the same learned evolution strategy can outperform established neuroevolution
baselines on supervised and continuous control tasks. As additional
contributions, we ablate the individual neural network components of our
method; reverse engineer the learned strategy into an explicit heuristic form,
which remains highly competitive; and show that it is possible to
self-referentially train an evolution strategy from scratch, with the learned
update rule used to drive the outer meta-learning loop.

该研究采用元学习来探寻对进化策略更有效的更新规则，并通过自我关注机制加以参数化，其结果显示新进化策略推广到未知的优化问题、种群大小和优化目标上表现出良好的性能。研究结果还表明，所学习的进化策略可以在监督学习和连续控制任务中优于传统的神经进化算法。