Testing the significance of a variable or group of variables $X$ for predicting a response $Y$, given additional covariates $Z$, is a ubiquitous task in statistics. A simple but common approach is to specify a linear model, and then test whether the regression coefficient for $X$ is non-zero. However, when the model is misspecified, the test may have poor power, for example when $X$ is involved in complex interactions, or lead to many false rejections. In this work we study the problem of testing the model-free null of conditional mean independence, i.e. that the conditional mean of $Y$ given $X$ and $Z$ does not depend on $X$. We propose a simple and general framework that can leverage flexible nonparametric or machine learning methods, such as additive models or random forests, to yield both robust error control and high power. The procedure involves using these methods to perform regressions, first to estimate a form of projection of $Y$ on $X$ and $Z$ using one half of the data, and then to estimate the expected conditional covariance between this projection and $Y$ on the remaining half of the data. While the approach is general, we show that a version of our procedure using spline regression achieves what we show is the minimax optimal rate in this nonparametric testing problem. Numerical experiments demonstrate the effectiveness of our approach both in terms of maintaining Type I error control, and power, compared to several existing approaches.

交叉验证是在许多非参数回归问题中调整参数选择的标准方法。然而，它在变化点回归中的使用不太常见，也许由于其预测误差的标准可能似乎允许小的虚假变化，因此不太适合估计变化点的数量和位置。我们表明，实际上，具有平方误差损失的交叉验证问题更严重，可以导致系统的减少或过度估计变化点的数量，以及在更改的简单设置中的平均功能的高度次优估计很容易检测到。我们提出了两种简单的方法来解决这些问题，第一个涉及使用绝对误差而不是平方误差损失，以及第二个涉及修改所用的熔断集。对于后者，我们提供了允许一致估计一般变更点估计程序的变化点数的条件。我们显示这些条件对于使用新结果的最佳分区满足其在提供错误数量的更改点时的性能。数值实验表明，特别是当错误分布良好的调整参数选择时，特别是使用经典调谐参数选择的绝对误差方法竞争，但可以在错过的模型中显着优于这些。 CRAN上的R包CrossValidationCP中提供了我们的方法。

我们提出了一种估计具有标称分类数据的高维线性模型的方法。我们的估算器，称为范围，通过使其相应的系数完全相等来融合水平。这是通过对分类变量的系数的阶数统计之间的差异之间的差异来实现这一点，从而聚类系数。我们提供了一种算法，用于精确和有效地计算在具有潜在许多级别的单个变量的情况下的总体上的最小值的全局最小值，并且在多变量情况下在块坐标血管下降过程中使用它。我们表明，利用未知级别融合的Oracle最小二乘解决方案是具有高概率的坐标血缘的极限点，只要真正的级别具有一定的最小分离;已知这些条件在单变量案例中最小。我们展示了在一系列实际和模拟数据集中的范围的有利性能。 R包的R包Catreg实现线性模型的范围，也可以在CRAN上提供逻辑回归的版本。

When robots learn reward functions using high capacity models that take raw state directly as input, they need to both learn a representation for what matters in the task -- the task ``features" -- as well as how to combine these features into a single objective. If they try to do both at once from input designed to teach the full reward function, it is easy to end up with a representation that contains spurious correlations in the data, which fails to generalize to new settings. Instead, our ultimate goal is to enable robots to identify and isolate the causal features that people actually care about and use when they represent states and behavior. Our idea is that we can tune into this representation by asking users what behaviors they consider similar: behaviors will be similar if the features that matter are similar, even if low-level behavior is different; conversely, behaviors will be different if even one of the features that matter differs. This, in turn, is what enables the robot to disambiguate between what needs to go into the representation versus what is spurious, as well as what aspects of behavior can be compressed together versus not. The notion of learning representations based on similarity has a nice parallel in contrastive learning, a self-supervised representation learning technique that maps visually similar data points to similar embeddings, where similarity is defined by a designer through data augmentation heuristics. By contrast, in order to learn the representations that people use, so we can learn their preferences and objectives, we use their definition of similarity. In simulation as well as in a user study, we show that learning through such similarity queries leads to representations that, while far from perfect, are indeed more generalizable than self-supervised and task-input alternatives.

网络脆弱性管理是网络安全操作中心（CSOC）的关键功能，该中心有助于保护组织免受计算机和网络系统上的网络攻击。对手比CSOC拥有不对称的优势，因为这些系统中的缺陷次数与安全团队的扩展率相比，在资源受限的环境中减轻它们的速度相比，其速度明显更高。当前的方法是确定性和一次性决策方法，在优先考虑和选择缓解漏洞时，这些方法不考虑未来的不确定性。这些方法还受到资源的亚最佳分布的约束，没有灵活性来调整其对脆弱性到达波动的响应的灵活性。我们提出了一个新颖的框架，深深的瓦尔曼，由深入的强化学习代理和整数编程方法组成，以填补网络脆弱性管理过程中的这一空白。我们的顺序决策框架首先确定在给定系统状态下不确定性下为缓解的近乎最佳的资源，然后确定最佳的缓解优先级漏洞实例。我们提出的框架优于当前方法在一年内观察到的模拟和现实世界脆弱性数据优先选择重要的组织特定漏洞。

目的：确定逼真，但是电磁图的计算上有效模型可用于预先列车，具有广泛的形态和特定于给定条件的形态和异常 - T波段（TWA）由于创伤后应激障碍，或重点 - 在稀有人的小型数据库上显着提高了性能。方法：使用先前经过验证的人工ECG模型，我们生成了180,000人的人工ECG，有或没有重要的TWA，具有不同的心率，呼吸率，TWA幅度和ECG形态。在70,000名患者中培训的DNN进行分类为25种不同的节奏，将输出层修改为二进制类（TWA或NO-TWA，或等效，PTSD或NO-PTSD），并对人工ECG进行转移学习。在最终转移学习步骤中，DNN在ECG的培训和交叉验证，从12个PTE和24个控件，用于使用三个数据库的所有组合。主要结果：通过进行转移学习步骤，使用预先培训的心律失常DNN，人工数据和真实的PTSD相关的心电图数据，发现了最佳性能的方法（AUROC = 0.77，精度= 0.72，F1-SCATE = 0.64） 。从训练中删除人工数据导致性能的最大下降。从培训中取出心律失常数据提供了适度但重要的，表现下降。最终模型在人工数据上显示出在性能下没有显着下降，表明没有过度拟合。意义：在医疗保健中，通常只有一小部分高质量数据和标签，或更大的数据库，质量较低（和较差的相关）标签。这里呈现的范式，涉及基于模型的性能提升，通过在大型现实人工数据库和部分相关的真实数据库上传输学习来提供解决方案。

我们描述了一种针对零售电子商务（电子商务）的需求而开发的新型决策问题。在使用物流和零售业商业合作者的同时，我们发现，从供应链中最适合的产品（称为成本为服务或CTS）的产品提供的产品成本是一个关键挑战。电子商务供应链的大规模，高性计，大大地理传播，使这一设置成为精心设计的数据驱动决策算法。在这项初步工作中，我们专注于在每次仓库中从任何仓库到多个客户提供多个产品的特定子问题。我们比较几个基线的相对性能和计算效率，包括启发式和混合整数线性规划。我们表明，基于加强学习的算法与这些政策具有竞争力，具有现实世界中有效扩大的潜力。

In this paper, we propose a novel technique, namely INVALIDATOR, to automatically assess the correctness of APR-generated patches via semantic and syntactic reasoning. INVALIDATOR reasons about program semantic via program invariants while it also captures program syntax via language semantic learned from large code corpus using the pre-trained language model. Given a buggy program and the developer-patched program, INVALIDATOR infers likely invariants on both programs. Then, INVALIDATOR determines that a APR-generated patch overfits if: (1) it violates correct specifications or (2) maintains errors behaviors of the original buggy program. In case our approach fails to determine an overfitting patch based on invariants, INVALIDATOR utilizes a trained model from labeled patches to assess patch correctness based on program syntax. The benefit of INVALIDATOR is three-fold. First, INVALIDATOR is able to leverage both semantic and syntactic reasoning to enhance its discriminant capability. Second, INVALIDATOR does not require new test cases to be generated but instead only relies on the current test suite and uses invariant inference to generalize the behaviors of a program. Third, INVALIDATOR is fully automated. We have conducted our experiments on a dataset of 885 patches generated on real-world programs in Defects4J. Experiment results show that INVALIDATOR correctly classified 79% overfitting patches, accounting for 23% more overfitting patches being detected by the best baseline. INVALIDATOR also substantially outperforms the best baselines by 14% and 19% in terms of Accuracy and F-Measure, respectively.

The latent space of autoencoders has been improved for clustering image data by jointly learning a t-distributed embedding with a clustering algorithm inspired by the neighborhood embedding concept proposed for data visualization. However, multivariate tabular data pose different challenges in representation learning than image data, where traditional machine learning is often superior to deep tabular data learning. In this paper, we address the challenges of learning tabular data in contrast to image data and present a novel Gaussian Cluster Embedding in Autoencoder Latent Space (G-CEALS) algorithm by replacing t-distributions with multivariate Gaussian clusters. Unlike current methods, the proposed approach independently defines the Gaussian embedding and the target cluster distribution to accommodate any clustering algorithm in representation learning. A trained G-CEALS model extracts a quality embedding for unseen test data. Based on the embedding clustering accuracy, the average rank of the proposed G-CEALS method is 1.4 (0.7), which is superior to all eight baseline clustering and cluster embedding methods on seven tabular data sets. This paper shows one of the first algorithms to jointly learn embedding and clustering to improve multivariate tabular data representation in downstream clustering.

We address the problem of extracting key steps from unlabeled procedural videos, motivated by the potential of Augmented Reality (AR) headsets to revolutionize job training and performance. We decompose the problem into two steps: representation learning and key steps extraction. We employ self-supervised representation learning via a training strategy that adapts off-the-shelf video features using a temporal module. Training implements self-supervised learning losses involving multiple cues such as appearance, motion and pose trajectories extracted from videos to learn generalizable representations. Our method extracts key steps via a tunable algorithm that clusters the representations extracted from procedural videos. We quantitatively evaluate our approach with key step localization and also demonstrate the effectiveness of the extracted representations on related downstream tasks like phase classification. Qualitative results demonstrate that the extracted key steps are meaningful to succinctly represent the procedural tasks.