We study the classic problem of prediction with expert advice under bandit
feedback. Our model assumes that one action, corresponding to the learner's
abstention from play, has no reward or loss on every trial. We propose the CBA
algorithm, which exploits this assumption to obtain reward bounds that can
significantly improve those of the classical Exp4 algorithm. We can view our
problem as the aggregation of confidence-rated predictors when the learner has
the option of abstention from play. Importantly, we are the first to achieve
bounds on the expected cumulative reward for general confidence-rated
predictors. In the special case of specialists we achieve a novel reward bound,
significantly improving previous bounds of SpecialistExp (treating abstention
as another action). As an example application, we discuss learning unions of
balls in a finite metric space. In this contextual setting, we devise an
efficient implementation of CBA, reducing the runtime from quadratic to almost
linear in the number of contexts. Preliminary experiments show that CBA
improves over existing bandit algorithms.

我们研究了专家意见下具有预测的经典问题，假设学习者选择不参与游戏的行动在每次试验中既没有奖励也没有损失，我们提出了 CBA 算法，利用这个假设获得了可以显著改善经典 Exp4 算法的奖励界限。我们将问题视为对置信度评估预测器进行合并，当学习者有选择不参与游戏的选项时。重要的是，我们是首次在普通置信度评估预测器上实现了累积期望奖励的界限。在专家预测器的特殊情况下，我们实现了一种新的奖励界限，显著改善了以前在特殊专家（将不参与视为另一种行动）上的界限。作为一个示例应用，我们讨论了在有限度量空间中学习球的并集。在这个背景设置中，我们设计了 CBA 的高效实现，将运行时间从二次降低到几乎线性与上下文数量相当。初步实验表明 CBA 在现有的 bandit 算法上有所改进。

基于专家建议的留投区随机选择问题

Bandits with Abstention under Expert Advice

Algorithmic fairness is receiving significant attention in the academic and
broader literature due to the increasing use of predictive algorithms,
including those based on artificial intelligence. One benefit of this trend is
that algorithm designers and users have a growing set of fairness measures to
choose from. However, this choice comes with the challenge of identifying how
the different fairness measures relate to one another, as well as the extent to
which they are compatible or mutually exclusive. We describe some of the most
widely used fairness metrics using a common mathematical framework and present
new results on the relationships among them. The results presented herein can
help place both specialists and non-specialists in a better position to
identify the metric best suited for their application and goals.

该研究利用一个数学框架描述了公平性评估中一些常用的指标，探讨了它们之间的关系，为算法开发者和用户提供指导。

公平度指标：比较分析

Fairness Metrics: A Comparative Analysis

We are proposing to use an ensemble of diverse specialists, where speciality
is defined according to the confusion matrix. Indeed, we observed that for
adversarial instances originating from a given class, labeling tend to be done
into a small subset of (incorrect) classes. Therefore, we argue that an
ensemble of specialists should be better able to identify and reject fooling
instances, with a high entropy (i.e., disagreement) over the decisions in the
presence of adversaries. Experimental results obtained confirm that
interpretation, opening a way to make the system more robust to adversarial
examples through a rejection mechanism, rather than trying to classify them
properly at any cost.

通过使用多个专家的集合，其中专业按混淆矩阵定义，我们发现在存在对抗实例的情况下，专家集合能更好地识别和拒绝愚弄实例，通过拒绝机制使系统更加鲁棒，而不是试图以任何代价正确地对抗其进行分类。