Explainability has become a valuable tool in the last few years, helping
humans better understand AI-guided decisions. However, the classic
explainability tools are sometimes quite limited when considering
high-dimensional inputs and neural network classifiers. We present a new
explainability method using theoretically proven high-dimensional properties in
neural network classifiers. We present two usages of it: 1) On the classical
sentiment analysis task for the IMDB reviews dataset, and 2) our
Malware-Detection task for our PowerShell scripts dataset.

使用理论证明的高维特性，在神经网络分类器中提出一种新的可解释性方法，并在经典情感分析任务和恶意软件检测任务中进行了实验。

高维文本分类器解释

Explaining high-dimensional text classifiers

The output distribution of a neural network (NN) over the entire input space
captures the complete input-output mapping relationship, offering insights
toward a more comprehensive NN understanding. Exhaustive enumeration or
traditional Monte Carlo methods for the entire input space can exhibit
impractical sampling time, especially for high-dimensional inputs. To make such
difficult sampling computationally feasible, in this paper, we propose a novel
Gradient-based Wang-Landau (GWL) sampler. We first draw the connection between
the output distribution of a NN and the density of states (DOS) of a physical
system. Then, we renovate the classic sampler for the DOS problem, the
Wang-Landau algorithm, by replacing its random proposals with gradient-based
Monte Carlo proposals. This way, our GWL sampler investigates the
under-explored subsets of the input space much more efficiently. Extensive
experiments have verified the accuracy of the output distribution generated by
GWL and also showcased several interesting findings - for example, in a binary
image classification task, both CNN and ResNet mapped the majority of human
unrecognizable images to very negative logit values.

本文提出了梯度基于的 Wang-Landau 采样算法，以更高效地探索神经网络的输出分布于输入空间之间的关系，实验结果在二元图像分类任务中，CNN 和 ResNet 把大多数不可识别的图片映射至负 logit 值。

基于梯度的 Wang-Landau 算法：神经网络在输入空间输出分布的新采样器

Gradient-based Wang-Landau Algorithm: A Novel Sampler for Output Distribution of Neural Networks over the Input Space

Most work on supervised learning research has focused on marginal
predictions. In decision problems, joint predictive distributions are essential
for good performance. Previous work has developed methods for assessing
low-order predictive distributions with inputs sampled i.i.d. from the testing
distribution. With low-dimensional inputs, these methods distinguish agents
that effectively estimate uncertainty from those that do not. We establish that
the predictive distribution order required for such differentiation increases
greatly with input dimension, rendering these methods impractical. To
accommodate high-dimensional inputs, we introduce \textit{dyadic sampling},
which focuses on predictive distributions associated with random \textit{pairs}
of inputs. We demonstrate that this approach efficiently distinguishes agents
in high-dimensional examples involving simple logistic regression as well as
complex synthetic and empirical data.

本文提出了一种称为 dyadic sampling 的方法，针对高维度的输入数据，能够有效地区分好坏的预测模型以及评估预测分布的方法或者对比其他算法的性能。

深度学习中高阶预测分布的评估

Evaluating High-Order Predictive Distributions in Deep Learning

Bayesian optimization (BO) has become an effective approach for black-box
function optimization problems when function evaluations are expensive and the
optimum can be achieved within a relatively small number of queries. However,
many cases, such as the ones with high-dimensional inputs, may require a much
larger number of observations for optimization. Despite an abundance of
observations thanks to parallel experiments, current BO techniques have been
limited to merely a few thousand observations. In this paper, we propose
ensemble Bayesian optimization (EBO) to address three current challenges in BO
simultaneously: (1) large-scale observations; (2) high dimensional input
spaces; and (3) selections of batch queries that balance quality and diversity.
The key idea of EBO is to operate on an ensemble of additive Gaussian process
models, each of which possesses a randomized strategy to divide and conquer. We
show unprecedented, previously impossible results of scaling up BO to tens of
thousands of observations within minutes of computation.

本文提出了一种基于集成高斯过程模型的贝叶斯优化算法（EBO），以解决黑盒函数优化问题中大规模观测、高维输入空间和批量查询质量与多样性平衡等三大挑战。结果表明，EBO 能在几分钟内扩展到数万次观测，同时取得了前所未有的优化效果。