The US federal government spends more than a trillion dollars per year on health care, largely provided by private third parties and reimbursed by the government. A major concern in this system is overbilling, waste and fraud by providers, who face incentives to misreport on their claims in order to receive higher payments. In this paper, we develop novel machine learning tools to identify providers that overbill Medicare, the US federal health insurance program for elderly adults and the disabled. Using large-scale Medicare claims data, we identify patterns consistent with fraud or overbilling among inpatient hospitalizations. Our proposed approach for Medicare fraud detection is fully unsupervised, not relying on any labeled training data, and is explainable to end users, providing reasoning and interpretable insights into the potentially suspicious behavior of the flagged providers. Data from the Department of Justice on providers facing anti-fraud lawsuits and several case studies validate our approach and findings both quantitatively and qualitatively.

给定无监督的离群检测（OD）算法，我们如何在没有任何标签的新数据集上优化其超参数（S）（hp）？在这项工作中，我们解决了针对无监督的OD问题的具有挑战性的超参数优化，并提出了基于元学习的第一种称为HPOD的系统方法。HPOD利用现有的OD基准数据集中大量HP的先前性能，并传输此信息以在没有标签的新数据集上启用HP评估。此外，HPOD适应基于顺序模型的优化（最初是监督的）优化，以有效地识别有希望的HP。广泛的实验表明，HPOD可以与深（例如健壮的自动编码器）和浅层（例如，局部离群因子（LOF）和隔离林（Iforest forest（iforeSt））OD算法一起使用，在离散和连续的HP空间上都超出了大量的基准范围比LOF和Iforest的默认HPS平均提高了58％和66％的性能。

自我监督学习（SSL）已成为一种有希望的替代方法，可以为现实世界任务创建监督信号，从而避免了仔细的标签成本。 SSL对于无监督的问题（例如异常检测（AD））特别有吸引力，在该问题中，标记的异常为确保，难以模拟甚至不存在的异常。基于SSL的AD（SSAD）已使用了大量的增强功能目录，并且最近的工作观察到，增强类型对性能有重大影响。这项工作是由这些工作的动机，将SSAD置于更大的镜头下，并通过对许多测试台进行广泛的实验仔细研究了数据增强在AD中的作用。我们的主要发现是，自我统治是另一个迄今为止的模型超参数，应仔细选择数据中真实异常的本质。也就是说，增强和基础异常机制之间的一致性是SSAD成功的关键，并且在缺乏SSL的情况下，SSL甚至会损害（！）检测性能。除了提出另一种SSAD方法外，我们的研究为对该成长中的地区提供了更好的了解，并为未来的研究提供了新的方向。

异常检测（OD）文献表现出许多适用于不同领域的算法。但是，鉴于新的检测任务，尚不清楚如何选择要使用的算法，也不清楚如何在无监督的设置中设置其超参数（S）（HPS）。 HP调整是一个不断增长的问题，基于深度学习的许多新探测器的到来。尽管它们具有诸如任务驱动的表示学习和端到端优化之类的吸引力，但深层模型附带了一长串HP。令人惊讶的是，在离群矿业文献中选择模型的问题是“房间里的大象”。释放深层方法的最大潜力的重要因素，但很少有人说或系统地解决这个问题。在本文的第一部分中，我们对Deep OD方法的HP敏感性进行了第一个大规模分析，并通过35,000多个训练有素的模型进行了定量证明模型选择是不可避免的。接下来，我们设计了一个称为Robod的HP刺激性和可扩展的深度高音模型，该模型以不同的HP配置组装模型，绕过选择瘫痪。重要的是，我们引入了新的策略来加快整体培训的速度，例如参数共享，批处理/同时培训和数据亚采样，使我们能够更少的参数培训较少的模型。图像和表格数据集的广泛实验表明，与其现代对应物相比，机器人可以实现并保留强大的最先进的检测性能，同时仅将2-10％的时间与独立的幼稚的超氛围相比，训练。

鉴于ICU（重症监护股）监测心脏病患者，用于大脑活动，我们如何尽早预测其健康结果？早期决策在许多应用中至关重要，例如，监测患者可能有助于早期干预和改善护理。另一方面，EEG数据的早期预测造成了几个挑战：（i）早期准确性权衡;观察更多数据通常会提高精度，但牺牲了，（ii）大规模（用于训练）和流传输（在线决策）数据处理，（iii）多变化（由于多个电极）和多长度（由于变化患者的逗留时间）时间序列。通过这种现实世界的应用程序，我们提供了从早期预测中耗尽的受益者，以及从错误分类到统一的区域特定目标中的成本。统一这两种数量允许我们直接估计单个目标（即益处），重要的是，准确地指示输出预测的时间：当益处估计变为肯定时。 Eventitter（a）是高效且快速的，在输入序列的数量中具有训练时间线性，并且可以实时运行以进行决策，（b）可以处理多变化和可变长度的时间序列，适用于患者数据和（c）是有效的，与竞争对手相比，提供高达2倍的时间，具有相同或更好的准确性。

消息传递神经网络（MPNNs）是格拉夫神经网络（GNN）的一个常见的类型，其中，每个节点的表示是通过聚集从表示其直接邻居（消息）类似于一个星形图案递归计算。 MPNNs的呼吁是有效的，可扩展的，怎么样，曾经它们的表现是由一阶Weisfeiler雷曼同构测试（1-WL）的上界。对此，之前的作品提出在可扩展性的成本极富表现力的模型，有时泛化性能。我们的工作表示这两个政权：我们介绍抬升任何MPNN更加传神，具有可扩展性有限的开销，大大提高了实用性能的总体框架。我们从星星图案一般的子模式（例如，K-egonets）在MPNNs扩展本地聚合实现这一点：在我们的框架中，每个节点表示被计算为周边诱发子的编码，而不是唯一的近邻编码（即一个明星）。我们选择子编码器是一个GNN（主要是MPNNs，考虑到可扩展性）来设计用作一个包装掀任何GNN的总体框架。我们把我们提出的方法GNN-AK（GNN为核心），作为框架用GNNS更换内核类似于卷积神经网络。从理论上讲，我们表明，我们的框架比1和2-WL确实更强大，并且不超过3-WL那么强大。我们还设计子取样策略，可大大降低内存占用和提高速度的同时保持性能。我们的方法将大利润率多家知名图形ML任务新的国家的最先进的性能;具体地，0.08 MAE锌，74.79％和86.887％的准确度上CIFAR10和分别PATTERN。

We investigate the representation power of graph neural networks in the semisupervised node classification task under heterophily or low homophily, i.e., in networks where connected nodes may have different class labels and dissimilar features. Many popular GNNs fail to generalize to this setting, and are even outperformed by models that ignore the graph structure (e.g., multilayer perceptrons). Motivated by this limitation, we identify a set of key designs-ego-and neighbor-embedding separation, higher-order neighborhoods, and combination of intermediate representations-that boost learning from the graph structure under heterophily. We combine them into a graph neural network, H 2 GCN, which we use as the base method to empirically evaluate the effectiveness of the identified designs. Going beyond the traditional benchmarks with strong homophily, our empirical analysis shows that the identified designs increase the accuracy of GNNs by up to 40% and 27% over models without them on synthetic and real networks with heterophily, respectively, and yield competitive performance under homophily.

Knowledge graphs, modeling multi-relational data, improve numerous applications such as question answering or graph logical reasoning. Many graph neural networks for such data emerged recently, often outperforming shallow architectures. However, the design of such multi-relational graph neural networks is ad-hoc, driven mainly by intuition and empirical insights. Up to now, their expressivity, their relation to each other, and their (practical) learning performance is poorly understood. Here, we initiate the study of deriving a more principled understanding of multi-relational graph neural networks. Namely, we investigate the limitations in the expressive power of the well-known Relational GCN and Compositional GCN architectures and shed some light on their practical learning performance. By aligning both architectures with a suitable version of the Weisfeiler-Leman test, we establish under which conditions both models have the same expressive power in distinguishing non-isomorphic (multi-relational) graphs or vertices with different structural roles. Further, by leveraging recent progress in designing expressive graph neural networks, we introduce the $k$-RN architecture that provably overcomes the expressiveness limitations of the above two architectures. Empirically, we confirm our theoretical findings in a vertex classification setting over small and large multi-relational graphs.

In recent years, graph neural networks (GNNs) have emerged as a promising tool for solving machine learning problems on graphs. Most GNNs are members of the family of message passing neural networks (MPNNs). There is a close connection between these models and the Weisfeiler-Leman (WL) test of isomorphism, an algorithm that can successfully test isomorphism for a broad class of graphs. Recently, much research has focused on measuring the expressive power of GNNs. For instance, it has been shown that standard MPNNs are at most as powerful as WL in terms of distinguishing non-isomorphic graphs. However, these studies have largely ignored the distances between the representations of nodes/graphs which are of paramount importance for learning tasks. In this paper, we define a distance function between nodes which is based on the hierarchy produced by the WL algorithm, and propose a model that learns representations which preserve those distances between nodes. Since the emerging hierarchy corresponds to a tree, to learn these representations, we capitalize on recent advances in the field of hyperbolic neural networks. We empirically evaluate the proposed model on standard node and graph classification datasets where it achieves competitive performance with state-of-the-art models.

机器学习分类器本质上是概率的，因此不可避免地涉及不确定性。预测特定输入正确的概率称为不确定性（或置信度）估计，对于风险管理至关重要。事后模型校准可以改善模型的不确定性估计，而无需重新培训，而无需更改模型。我们的工作为不确定性估计提出了一种基于几何的方法。粗略地说，我们使用现有训练输入的当前输入的几何距离作为估计不确定性的信号，然后使用标准的事后校准技术校准该信号（而不是模型的估计）。我们表明，通过广泛评估多个数据集和模型，我们的方法比最近提出的方法产生更好的不确定性估计。此外，我们还证明了在接近实时应用程序中执行方法的可能性。我们的代码可在我们的github https://github.com/nosleepdeveloper/geometric-calibrator上找到。