We consider the problem of learning the structure underlying a Gaussian graphical model when the variables (or subsets thereof) are corrupted by independent noise. A recent line of work establishes that even for tree-structured graphical models, only partial structure recovery is possible and goes on to devise algorithms to identify the structure up to an (unavoidable) equivalence class of trees. We extend these results beyond trees and consider the model selection problem under noise for non tree-structured graphs, as tree graphs cannot model several real-world scenarios. Although unidentifiable, we show that, like the tree-structured graphs, the ambiguity is limited to an equivalence class. This limited ambiguity can help provide meaningful clustering information (even with noise), which is helpful in computer and social networks, protein-protein interaction networks, and power networks. Furthermore, we devise an algorithm based on a novel ancestral testing method for recovering the equivalence class. We complement these results with finite sample guarantees for the algorithm in the high-dimensional regime.

当无法获得网络结构知识并且知识仅限于粗略摘要时，我们考虑大规模线性网络动力学系统的可控性。我们提供条件下，通过（合成，减少）粗尺度系统的平均可控性可以很好地近似细尺度系统的平均可控性。为此，我们需要了解精细尺度网络的某些固有参数结构，这使这种类型的近似结构成为可能。因此，我们假设潜在的细尺度网络是由随机块模型（SBM）生成的 - 经常在社区检测中进行研究。然后，我们提供了一种算法，该算法直接使用SBM的粗摘要直接估算细尺度系统的平均可控性。我们的分析表明，基本结构（例如，内建立的社区）的必要性能够准确地量化从粗体表征的网络动力学中的可控性。我们还将我们的方法与减少订单方法的方法进行比较，并突出显示了双方都可以相互表现的制度。最后，我们提供了模拟，以确认网络大小和密度不同尺度的理论结果，以及捕获粗略摘要中保留多少社区结构的参数。

众所周知，许多网络系统，例如电网，大脑和舆论动态社交网络，都可以遵守保护法。这种现象的例子包括电网中的基尔乔夫法律和社交网络中的意见共识。网络系统中的保护定律可以建模为$ x = b^{*} y $的平衡方程，其中$ b^{*} $的稀疏模式捕获了网络的连接，$ y，x \在\ mathbb {r}^p $中分别是节点上“电势”和“注入流”的向量。节点电位$ y $会导致跨边缘的流量，并且在节点上注入的流量$ x $是网络动力学的无关紧要的。在几个实用的系统中，网络结构通常是未知的，需要从数据估算。为此，可以访问节点电位$ y $的样本，但只有节点注射$ x $的统计信息。在这个重要问题的激励下，我们研究了$ n $ y $ y $ y $ y $ y $ y $ y $ y $ b^{*} $稀疏结构的估计，假设节点注射$ x $遵循高斯分布，并带有已知的发行协方差$ \ sigma_x $。我们建议在高维度中为此问题的新$ \ ell_ {1} $ - 正则最大似然估计器，网络的大小$ p $大于样本量$ n $。我们表明，此优化问题是目标中的凸，并接受了独特的解决方案。在新的相互不一致的条件下，我们在三重$（n，p，d）$上建立了足够的条件，对于$ b^{*} $的精确稀疏恢复是可能的； $ d $是图的程度。我们还建立了在元素最大，Frobenius和运营商规范中回收$ b^{*} $的保证。最后，我们通过对拟议估计量对合成和现实世界数据的性能进行实验验证来补充这些理论结果。

电力系统容易出现各种事件（例如线路旅行和发电损失），而在情境意识，可靠性和安全性方面，对此类事件的实时识别至关重要。使用来自多个同步管理器的测量值，即相量测量单元（PMU），我们建议通过基于模态动力学提取特征来识别事件。我们将这种基于物理学的特征提取方法与机器学习结合在一起，以区分不同的事件类型。包括每个PMU的所有测量通道都允许利用各种功能，但还需要在高维空间上学习分类模型。为了解决此问题，实现了各种功能选择方法，以选择最佳功能子集。使用获得的功能子集，我们研究了两个众所周知的分类模型的性能，即逻辑回归（LR）和支持向量机（SVM），以识别两个数据集中的发电损失和线路跳闸事件。第一个数据集是从得克萨斯州2000-Bus合成网格中的模拟发电损失和线路跳闸事件中获得的。第二个是专有数据集，其标记事件是从美国的大型公用事业中获得的，涉及近500 pmus的测量。我们的结果表明，所提出的框架有望确定两种类型的事件。

两种样本测试评估两个样品是否是相同分布（零假设）或两种不同分布（替代假设）的实现。在传统的本问题的制定中，统计学家可以访问测量（特征变量）和组变量（标签变量）。但是，在几个重要的应用程序中，可以轻松测量特征变量，但二进制标签变量是未知的并且获得昂贵的。在本文中，我们考虑了经典的两个样本测试问题的这一重要变化，并将其构成，作为在执行两个样本测试的服务中仅获得少量样品的标签的问题。我们设计了一个标签高效的三阶段框架：首先，分类器培训，采用均匀标记为模拟标签的后验概率;其次，将一个创新的查询计划被称为\ emph {bimodal查询}用于查询来自两个类别的样本标签，最大的后验概率，最后，对查询样本进行了经典的弗里德曼-RAFSKY（FR）两样测试。我们的理论分析表明，在合理的条件下，双峰查询对于FR测试是最佳的，并且三阶段框架控制I误差。对合成，基准和应用程序特定数据集进行的广泛实验表明，三阶段框架在控制I错误的统一查询和确定的基于标签上的统一查询和确定性的查询中的II型误差减少。

We introduce a tunable loss function called $\alpha$-loss, parameterized by $\alpha \in (0,\infty]$, which interpolates between the exponential loss ($\alpha = 1/2$), the log-loss ($\alpha = 1$), and the 0-1 loss ($\alpha = \infty$), for the machine learning setting of classification. Theoretically, we illustrate a fundamental connection between $\alpha$-loss and Arimoto conditional entropy, verify the classification-calibration of $\alpha$-loss in order to demonstrate asymptotic optimality via Rademacher complexity generalization techniques, and build-upon a notion called strictly local quasi-convexity in order to quantitatively characterize the optimization landscape of $\alpha$-loss. Practically, we perform class imbalance, robustness, and classification experiments on benchmark image datasets using convolutional-neural-networks. Our main practical conclusion is that certain tasks may benefit from tuning $\alpha$-loss away from log-loss ($\alpha = 1$), and to this end we provide simple heuristics for the practitioner. In particular, navigating the $\alpha$ hyperparameter can readily provide superior model robustness to label flips ($\alpha > 1$) and sensitivity to imbalanced classes ($\alpha < 1$).

Graph Neural Networks (GNNs) have become increasingly important in recent years due to their state-of-the-art performance on many important downstream applications. Existing GNNs have mostly focused on learning a single node representation, despite that a node often exhibits polysemous behavior in different contexts. In this work, we develop a persona-based graph neural network framework called PersonaSAGE that learns multiple persona-based embeddings for each node in the graph. Such disentangled representations are more interpretable and useful than a single embedding. Furthermore, PersonaSAGE learns the appropriate set of persona embeddings for each node in the graph, and every node can have a different number of assigned persona embeddings. The framework is flexible enough and the general design helps in the wide applicability of the learned embeddings to suit the domain. We utilize publicly available benchmark datasets to evaluate our approach and against a variety of baselines. The experiments demonstrate the effectiveness of PersonaSAGE for a variety of important tasks including link prediction where we achieve an average gain of 15% while remaining competitive for node classification. Finally, we also demonstrate the utility of PersonaSAGE with a case study for personalized recommendation of different entity types in a data management platform.

We introduce camouflaged data poisoning attacks, a new attack vector that arises in the context of machine unlearning and other settings when model retraining may be induced. An adversary first adds a few carefully crafted points to the training dataset such that the impact on the model's predictions is minimal. The adversary subsequently triggers a request to remove a subset of the introduced points at which point the attack is unleashed and the model's predictions are negatively affected. In particular, we consider clean-label targeted attacks (in which the goal is to cause the model to misclassify a specific test point) on datasets including CIFAR-10, Imagenette, and Imagewoof. This attack is realized by constructing camouflage datapoints that mask the effect of a poisoned dataset.

Light guide plates are essential optical components widely used in a diverse range of applications ranging from medical lighting fixtures to back-lit TV displays. In this work, we introduce a fully-integrated, high-throughput, high-performance deep learning-driven workflow for light guide plate surface visual quality inspection (VQI) tailored for real-world manufacturing environments. To enable automated VQI on the edge computing within the fully-integrated VQI system, a highly compact deep anti-aliased attention condenser neural network (which we name LightDefectNet) tailored specifically for light guide plate surface defect detection in resource-constrained scenarios was created via machine-driven design exploration with computational and "best-practices" constraints as well as L_1 paired classification discrepancy loss. Experiments show that LightDetectNet achieves a detection accuracy of ~98.2% on the LGPSDD benchmark while having just 770K parameters (~33X and ~6.9X lower than ResNet-50 and EfficientNet-B0, respectively) and ~93M FLOPs (~88X and ~8.4X lower than ResNet-50 and EfficientNet-B0, respectively) and ~8.8X faster inference speed than EfficientNet-B0 on an embedded ARM processor. As such, the proposed deep learning-driven workflow, integrated with the aforementioned LightDefectNet neural network, is highly suited for high-throughput, high-performance light plate surface VQI within real-world manufacturing environments.

Deep neural networks (DNN) are prone to miscalibrated predictions, often exhibiting a mismatch between the predicted output and the associated confidence scores. Contemporary model calibration techniques mitigate the problem of overconfident predictions by pushing down the confidence of the winning class while increasing the confidence of the remaining classes across all test samples. However, from a deployment perspective, an ideal model is desired to (i) generate well-calibrated predictions for high-confidence samples with predicted probability say >0.95, and (ii) generate a higher proportion of legitimate high-confidence samples. To this end, we propose a novel regularization technique that can be used with classification losses, leading to state-of-the-art calibrated predictions at test time; From a deployment standpoint in safety-critical applications, only high-confidence samples from a well-calibrated model are of interest, as the remaining samples have to undergo manual inspection. Predictive confidence reduction of these potentially ``high-confidence samples'' is a downside of existing calibration approaches. We mitigate this by proposing a dynamic train-time data pruning strategy that prunes low-confidence samples every few epochs, providing an increase in "confident yet calibrated samples". We demonstrate state-of-the-art calibration performance across image classification benchmarks, reducing training time without much compromise in accuracy. We provide insights into why our dynamic pruning strategy that prunes low-confidence training samples leads to an increase in high-confidence samples at test time.