能够从专家推断出第二意见的自动决策支持系统可能有助于更有效地分配资源；他们可以帮助决定何时何地寻求第二意见。在本文中，我们从反事实推断的角度研究了这种类型的支持系统的设计。我们专注于多类分类设置，并首先表明，如果专家自行做出预测，那么产生其预测的基本因果机制就需要满足理想的设定不变属性。此外，我们表明，对于满足该特性的任何因果机制，存在一种等效机制，其中每个专家的预测是由由共同噪声控制的独立亚机制产生的。这激发了设定不变的gumbel-max结构因果模型的设计，其中管理模型的亚机制的噪声结构取决于专家之间相似性的直觉概念，可以从数据估算。合成数据和真实数据的实验表明，我们的模型可用于比其非伴侣对应物更准确地推断第二个意见。

许多选择过程，例如寻找有资格参加医学试验的患者或在搜索引擎中检索管道的供应，其中包括多个阶段，初始筛查阶段将资源集中在候选名单上最有前途的候选人。在本文中，我们研究了保证筛选分类器可以提供的内容，而不是手动构造还是训练。我们发现当前的解决方案不享受无分配的理论保证 - 我们表明，通常，即使对于完美校准的分类器，也总是存在特定的候选人库，其候选名单是次优的。然后，我们开发了一种无分布的筛选算法（称为校准子集选择（CSS）），给定任何分类器和一定数量的校准数据，发现近乎最佳的候选者候选人，这些候选者包含预期的预期数量的合格候选者。此外，我们表明，在特定组中多次校准给定分类器的CSS变体可以创建具有可证明多样性保证的候选名单。关于美国人口普查调查数据的实验验证了我们的理论结果，并表明我们算法提供的候选名单优于几个竞争基线提供的列表。

Automated decision support systems promise to help human experts solve tasks more efficiently and accurately. However, existing systems typically require experts to understand when to cede agency to the system or when to exercise their own agency. Moreover, if the experts develop a misplaced trust in the system, their performance may worsen. In this work, we lift the above requirement and develop automated decision support systems that, by design, do not require experts to understand when each of their recommendations is accurate to improve their performance. To this end, we focus on multiclass classification tasks and consider an automated decision support system that, for each data sample, uses a classifier to recommend a subset of labels to a human expert. We first show that, by looking at the design of such a system from the perspective of conformal prediction, we can ensure that the probability that the recommended subset of labels contains the true label matches almost exactly a target probability value with high probability. Then, we develop an efficient and near-optimal search method to find the target probability value under which the expert benefits the most from using our system. Experiments on synthetic and real data demonstrate that our system can help the experts make more accurate predictions and is robust to the accuracy of the classifier it relies on.

基于时间点过程的机器学习模型是在连续时间内涉及离散事件的各种应用中的技术的技术。但是，这些模型缺乏回答反事实问题的能力，因为这些模型正在用于通知有针对性的干预措施越来越相关。在这项工作中，我们的目标是填补这个差距。为此，我们首先开发一种因果点流程的变薄模型，这些过程构建在Gumbel-Max结构因果模型上。该模型满足所需的反事实单调性条件，足以识别稀疏过程中的反事实动态。然后，考虑到具有给定强度函数的时间点处理的观察到实现，我们开发了一种采样算法，该采样算法使用上述变化的因果模型和叠加定理来模拟给定的替代强度函数下的时间点处理的反事实实现。使用综合性和实际流行病学数据的仿真实验表明，我们的算法提供的反事实实现可以提供有价值的见解来增强目标干预措施。

Automated data-driven decision making systems are increasingly being used to assist, or even replace humans in many settings. These systems function by learning from historical decisions, often taken by humans. In order to maximize the utility of these systems (or, classifiers), their training involves minimizing the errors (or, misclassifications) over the given historical data. However, it is quite possible that the optimally trained classifier makes decisions for people belonging to different social groups with different misclassification rates (e.g., misclassification rates for females are higher than for males), thereby placing these groups at an unfair disadvantage. To account for and avoid such unfairness, in this paper, we introduce a new notion of unfairness, disparate mistreatment, which is defined in terms of misclassification rates. We then propose intuitive measures of disparate mistreatment for decision boundary-based classifiers, which can be easily incorporated into their formulation as convex-concave constraints. Experiments on synthetic as well as real world datasets show that our methodology is effective at avoiding disparate mistreatment, often at a small cost in terms of accuracy.

Algorithmic decision making systems are ubiquitous across a wide variety of online as well as offline services. These systems rely on complex learning methods and vast amounts of data to optimize the service functionality, satisfaction of the end user and profitability. However, there is a growing concern that these automated decisions can lead, even in the absence of intent, to a lack of fairness, i.e., their outcomes can disproportionately hurt (or, benefit) particular groups of people sharing one or more sensitive attributes (e.g., race, sex). In this paper, we introduce a flexible mechanism to design fair classifiers by leveraging a novel intuitive measure of decision boundary (un)fairness. We instantiate this mechanism with two well-known classifiers, logistic regression and support vector machines, and show on real-world data that our mechanism allows for a fine-grained control on the degree of fairness, often at a small cost in terms of accuracy. A Python implementation of our mechanism is available at fate-computing.mpi-sws.org

可以使用具有快速有效分割网络的深度学习方法来实施医疗图像分割。单板计算机（SBC）由于内存和处理限制而难以用于训练深网。诸如Google Edge TPU之类的特定硬件使其适合使用复杂的预训练网络进行实时预测。在这项工作中，我们研究了两个SBC的性能，具有和不进行硬件加速度进行底面图像分割，尽管这项研究的结论可以通过其他类型的医学图像的深层神经网络应用于分割。为了测试硬件加速的好处，我们使用先前已发布的工作中的网络和数据集，并通过使用具有超声甲状腺图像的数据集进行测试来概括它们。我们在SBC中测量预测时间，并将其与基于云的TPU系统进行比较。结果表明，使用Edge TPU，机器学习加速SBC的可行性可加速光盘和杯赛分段，每图像可获得低于25毫秒的时间。

Credit scoring models are the primary instrument used by financial institutions to manage credit risk. The scarcity of research on behavioral scoring is due to the difficult data access. Financial institutions have to maintain the privacy and security of borrowers' information refrain them from collaborating in research initiatives. In this work, we present a methodology that allows us to evaluate the performance of models trained with synthetic data when they are applied to real-world data. Our results show that synthetic data quality is increasingly poor when the number of attributes increases. However, creditworthiness assessment models trained with synthetic data show a reduction of 3\% of AUC and 6\% of KS when compared with models trained with real data. These results have a significant impact since they encourage credit risk investigation from synthetic data, making it possible to maintain borrowers' privacy and to address problems that until now have been hampered by the availability of information.

Novel topological spin textures, such as magnetic skyrmions, benefit from their inherent stability, acting as the ground state in several magnetic systems. In the current study of atomic monolayer magnetic materials, reasonable initial guesses are still needed to search for those magnetic patterns. This situation underlines the need to develop a more effective way to identify the ground states. To solve this problem, in this work, we propose a genetic-tunneling-driven variance-controlled optimization approach, which combines a local energy minimizer back-end and a metaheuristic global searching front-end. This algorithm is an effective optimization solution for searching for magnetic ground states at extremely low temperatures and is also robust for finding low-energy degenerated states at finite temperatures. We demonstrate here the success of this method in searching for magnetic ground states of 2D monolayer systems with both artificial and calculated interactions from density functional theory. It is also worth noting that the inherent concurrent property of this algorithm can significantly decrease the execution time. In conclusion, our proposed method builds a useful tool for low-dimensional magnetic system energy optimization.

We present a Machine Learning (ML) study case to illustrate the challenges of clinical translation for a real-time AI-empowered echocardiography system with data of ICU patients in LMICs. Such ML case study includes data preparation, curation and labelling from 2D Ultrasound videos of 31 ICU patients in LMICs and model selection, validation and deployment of three thinner neural networks to classify apical four-chamber view. Results of the ML heuristics showed the promising implementation, validation and application of thinner networks to classify 4CV with limited datasets. We conclude this work mentioning the need for (a) datasets to improve diversity of demographics, diseases, and (b) the need of further investigations of thinner models to be run and implemented in low-cost hardware to be clinically translated in the ICU in LMICs. The code and other resources to reproduce this work are available at https://github.com/vital-ultrasound/ai-assisted-echocardiography-for-low-resource-countries.