Domain generalization (DG) aims to train a model to perform well in unseen domains under different distributions. This paper considers a more realistic yet more challenging scenario,namely Single Domain Generalization (Single-DG), where only a single source domain is available for training. To tackle this challenge, we first try to understand when neural networks fail to generalize? We empirically ascertain a property of a model that correlates strongly with its generalization that we coin as "model sensitivity". Based on our analysis, we propose a novel strategy of Spectral Adversarial Data Augmentation (SADA) to generate augmented images targeted at the highly sensitive frequencies. Models trained with these hard-to-learn samples can effectively suppress the sensitivity in the frequency space, which leads to improved generalization performance. Extensive experiments on multiple public datasets demonstrate the superiority of our approach, which surpasses the state-of-the-art single-DG methods.

本文研究了在因果图形模型中设计最佳干预措施序列的问题，以最大程度地减少对事后最佳干预的累积后悔。自然，这是一个因果匪徒问题。重点是线性结构方程模型（SEM）和软干预措施的因果匪徒。假定该图的结构是已知的，并且具有$ n $节点。每个节点都假定使用两种线性机制，一种软干预和一种观察性，产生了$ 2^n $可能的干预措施。现有的因果匪徒算法假设，至少完全指定了奖励节点父母的介入分布。但是，有$ 2^n $这样的分布（一个与每个干预措施相对应），即使在中等尺寸的图中也变得越来越高。本文分配了知道这些分布的假设。提出了两种算法，用于常见者（基于UCB）和贝叶斯（基于汤普森采样）的设置。这些算法的关键思想是避免直接估计$ 2^n $奖励分布，而是估算完全指定SEMS（$ n $线性）的参数，并使用它们来计算奖励。在这两种算法中，在噪声和参数空间的有界假设下，累积遗憾的是$ \ tilde {\ cal o}（（（2d）^l l \ sqrt {t}）$，其中$ d $是图的最高度和$ l $是其最长因果路径的长度。

考虑$ k $过程，每个过程都会生成一系列相同和独立的随机变量。这些过程的概率度量具有必须估计的随机参数。具体而言，它们共享一个参数$ \ theta $，所有概率度量共同。此外，每个过程$ i \ in \ {1，\ dots，k \} $都有一个私有参数$ \ alpha_i $。目的是设计一种主动采样算法，以顺序估算这些参数，以形成所有样品数量最少的共享和私有参数的可靠估计。该采样算法具有三个关键组件：（i）〜数据驱动的采样决策，随着时间的推移，该决策逐渐指定应选择哪些$ k $过程进行采样； （ii）〜停止该过程的时间，该过程指定何时累积数据足以形成可靠的估计并终止采样过程； （iii）〜所有共享和私人参数的估计器。由于已知的顺序估计在分析上是棘手的，因此本文采用\ emph {条件}估计成本函数，从而导致了顺序估计方法，该方法最近被证明可以进行拖延分析。划定了渐近的最佳决策规则（采样，停止和估计），并提供了数值实验，以将所提出的程序的疗效和质量与相关方法进行比较。

本文研究了固定置信度设置中随机多臂匪徒中最佳的手臂识别（BAI）问题。考虑到指数匪徒的一般类。指数匪徒家族的最先进算法面临计算挑战。为了缓解这些挑战，提出了一个新颖的框架，该框架将BAI问题视为顺序假设测试，并且可以适合针对指数的土匪家族的可拖动分析。基于此框架，设计了BAI算法，以利用规范顺序概率比测试。该算法在两种设置中都具有三个功能：（1）其样本复杂性在渐近上是最佳的，（2）保证它是$ \ delta- $ pac，（3）它解决了最先进的计算挑战 - 艺术方法。具体而言，这些方法仅专注于高斯环境，需要从汤普森（Thompson）的手臂上进行采样，而这些方法被认为是最好的和挑战者的手臂。本文分析表明，识别挑战者在计算上是昂贵的，并且提出的算法对其进行了规定。最后，提供了数值实验来支持分析。

本文考虑了从观察和介入数据估算因果导向的非循环图中未知干预目标的问题。重点是线性结构方程模型（SEM）中的软干预。目前对因果结构的方法学习使用已知的干预目标或使用假设测试来发现即使是线性SEM也可以发现未知的干预目标。这严重限制了它们的可扩展性和样本复杂性。本文提出了一种可扩展和高效的算法，始终识别所有干预目标。关键思想是从与观察和介入数据集相关联的精度矩阵之间的差异来估计干预站点。它涉及反复估计不同亚空间子集中的这些站点。该算法的算法还可用于将给定的观察马尔可夫等效类更新为介入马尔可夫等价类。在分析地建立一致性，马尔可夫等效和采样复杂性。最后，实际和合成数据的仿真结果展示了所提出的可扩展因果结构恢复方法的增益。算法的实现和重现仿真结果的代码可用于\ url {https://github.com/bvarici/intervention- istimation}。

本文调查$ \纺织品{污染} $随机多臂爆炸中最佳臂识别问题。在此设置中，从任何臂获得的奖励由来自概率$ \ varepsilon $的对抗性模型的样本所取代。考虑了固定的置信度（无限地平线）设置，其中学习者的目标是识别最大的平均值。由于奖励的对抗污染，每个ARM的平均值仅部分可识别。本文提出了两种算法，基于连续消除的基于间隙的算法和一个，以便在亚高斯匪徒中最佳臂识别。这些算法涉及平均估计，从渐近估计的估计值达到真实均值的偏差上实现最佳误差保证。此外，这些算法渐近地实现了最佳的样本复杂性。具体地，对于基于差距的算法，样本复杂性呈渐近最佳到恒定因子，而对于基于连续的基于算法，​​它是最佳的对数因子。最后，提供了数值实验以说明与现有基线相比的算法的增益。

Existing automated techniques for software documentation typically attempt to reason between two main sources of information: code and natural language. However, this reasoning process is often complicated by the lexical gap between more abstract natural language and more structured programming languages. One potential bridge for this gap is the Graphical User Interface (GUI), as GUIs inherently encode salient information about underlying program functionality into rich, pixel-based data representations. This paper offers one of the first comprehensive empirical investigations into the connection between GUIs and functional, natural language descriptions of software. First, we collect, analyze, and open source a large dataset of functional GUI descriptions consisting of 45,998 descriptions for 10,204 screenshots from popular Android applications. The descriptions were obtained from human labelers and underwent several quality control mechanisms. To gain insight into the representational potential of GUIs, we investigate the ability of four Neural Image Captioning models to predict natural language descriptions of varying granularity when provided a screenshot as input. We evaluate these models quantitatively, using common machine translation metrics, and qualitatively through a large-scale user study. Finally, we offer learned lessons and a discussion of the potential shown by multimodal models to enhance future techniques for automated software documentation.

In this paper, we reduce the complexity of approximating the correlation clustering problem from $O(m\times\left( 2+ \alpha (G) \right)+n)$ to $O(m+n)$ for any given value of $\varepsilon$ for a complete signed graph with $n$ vertices and $m$ positive edges where $\alpha(G)$ is the arboricity of the graph. Our approach gives the same output as the original algorithm and makes it possible to implement the algorithm in a full dynamic setting where edge sign flipping and vertex addition/removal are allowed. Constructing this index costs $O(m)$ memory and $O(m\times\alpha(G))$ time. We also studied the structural properties of the non-agreement measure used in the approximation algorithm. The theoretical results are accompanied by a full set of experiments concerning seven real-world graphs. These results shows superiority of our index-based algorithm to the non-index one by a decrease of %34 in time on average.

This paper proposes a novel self-supervised based Cut-and-Paste GAN to perform foreground object segmentation and generate realistic composite images without manual annotations. We accomplish this goal by a simple yet effective self-supervised approach coupled with the U-Net based discriminator. The proposed method extends the ability of the standard discriminators to learn not only the global data representations via classification (real/fake) but also learn semantic and structural information through pseudo labels created using the self-supervised task. The proposed method empowers the generator to create meaningful masks by forcing it to learn informative per-pixel as well as global image feedback from the discriminator. Our experiments demonstrate that our proposed method significantly outperforms the state-of-the-art methods on the standard benchmark datasets.

Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such. Especially in the bio-medical domain, annotations are subjective and suffer from low inter- and intra-rater reliability. Since annotations only reflect the annotation entity's interpretation of the real world, this can lead to sub-optimal predictions even though the model achieves high similarity scores. Here, the theoretical concept of Peak Ground Truth (PGT) is introduced. PGT marks the point beyond which an increase in similarity with the reference annotation stops translating to better Real World Model Performance (RWMP). Additionally, a quantitative technique to approximate PGT by computing inter- and intra-rater reliability is proposed. Finally, three categories of PGT-aware strategies to evaluate and improve model performance are reviewed.