Machine learning (ML) classification is increasingly used in safety-critical
systems. Protecting ML classifiers from adversarial examples is crucial. We
propose that the main threat is that of an attacker perturbing a confidently
classified input to produce a confident misclassification. To protect against
this we devise an adversarial bound (AB) for a Gaussian process classifier,
that holds for the entire input domain, bounding the potential for any future
adversarial method to cause such misclassification. This is a formal guarantee
of robustness, not just an empirically derived result. We investigate how to
configure the classifier to maximise the bound, including the use of a sparse
approximation, leading to the method producing a practical, useful and provably
robust classifier, which we test using a variety of datasets.

本文提出了一种防止黑客攻击，保护机器学习分类器免受对抗性样本的影响的方法，通过一个对抗边界来限制潜在的对抗攻击产生误分类的能力并测试了其效果。

高斯过程分类的对抗性漏洞边界

Adversarial Vulnerability Bounds for Gaussian Process Classification

Machine learning models are vulnerable to adversarial examples: minor
perturbations to input samples intended to deliberately cause
misclassification. While an obvious security threat, adversarial examples yield
as well insights about the applied model itself. We investigate adversarial
examples in the context of Bayesian neural network's (BNN's) uncertainty
measures. As these measures are highly non-smooth, we use a smooth Gaussian
process classifier (GPC) as substitute. We show that both confidence and
uncertainty can be unsuspicious even if the output is wrong. Intriguingly, we
find subtle differences in the features influencing uncertainty and confidence
for most tasks.

研究机器学习模型中的对抗样本和贝叶斯神经网络的不确定性测量，并发现置信度和不确定性可能是无可疑的，即使输出是错误的；同时，对大多数任务，我们在影响不确定性和置信度方面发现微妙的差异。

对抗环境下模型不确定性的局限性

The Limitations of Model Uncertainty in Adversarial Settings

The learning with privileged information setting has recently attracted a lot
of attention within the machine learning community, as it allows the
integration of additional knowledge into the training process of a classifier,
even when this comes in the form of a data modality that is not available at
test time. Here, we show that privileged information can naturally be treated
as noise in the latent function of a Gaussian Process classifier (GPC). That
is, in contrast to the standard GPC setting, the latent function is not just a
nuisance but a feature: it becomes a natural measure of confidence about the
training data by modulating the slope of the GPC sigmoid likelihood function.
Extensive experiments on public datasets show that the proposed GPC method
using privileged noise, called GPC+, improves over a standard GPC without
privileged knowledge, and also over the current state-of-the-art SVM-based
method, SVM+. Moreover, we show that advanced neural networks and deep learning
methods can be compressed as privileged information.

利用高特权信息训练高斯过程分类器（GPC），提高分类精度，并证明高斯过程的潜在函数可以作为高特权信息的一种处理方式，通过调整 GPC Sigmoid 概率函数的斜率，提高 GPC + 方法的性能。

基于特权噪音的高斯过程分类

Mind the Nuisance: Gaussian Process Classification using Privileged  Noise

Information theoretic active learning has been widely studied for
probabilistic models. For simple regression an optimal myopic policy is easily
tractable. However, for other tasks and with more complex models, such as
classification with nonparametric models, the optimal solution is harder to
compute. Current approaches make approximations to achieve tractability. We
propose an approach that expresses information gain in terms of predictive
entropies, and apply this method to the Gaussian Process Classifier (GPC). Our
approach makes minimal approximations to the full information theoretic
objective. Our experimental performance compares favourably to many popular
active learning algorithms, and has equal or lower computational complexity. We
compare well to decision theoretic approaches also, which are privy to more
information and require much more computational time. Secondly, by developing
further a reformulation of binary preference learning to a classification
problem, we extend our algorithm to Gaussian Process preference learning.

通过使用预测熵计算信息增益，我们提出了一种方法来优化高斯过程分类器的主动学习，同时也将其扩展到高斯过程偏好学习，相比其他主动学习和决策理论方法，我们的方法可以在相等或更低的计算复杂度下获得相同的实验性能。