我们的目标是使用自由文本和结构数据的组合构建分类模型。为此，我们通过文本句子，DataWords表示结构化数据，使类似的数据项映射到同一个句子中。这允许通过仅使用文本建模算法来建立文本和结构化数据的混合。有几个例子说明了通过首先运行的提取工具（命名实体识别）来提高文本分类性能，然后将输出转换为DataWords，并将DataWords添加到原始文本 - 在模型构建和分类之前。这种方法还允许我们在自由文本和结构化数据方面为推断产生解释。

We study the hidden-action principal-agent problem in an online setting. In each round, the principal posts a contract that specifies the payment to the agent based on each outcome. The agent then makes a strategic choice of action that maximizes her own utility, but the action is not directly observable by the principal. The principal observes the outcome and receives utility from the agent's choice of action. Based on past observations, the principal dynamically adjusts the contracts with the goal of maximizing her utility. We introduce an online learning algorithm and provide an upper bound on its Stackelberg regret. We show that when the contract space is $[0,1]^m$, the Stackelberg regret is upper bounded by $\widetilde O(\sqrt{m} \cdot T^{1-C/m})$, and lower bounded by $\Omega(T^{1-1/(m+2)})$. This result shows that exponential-in-$m$ samples are both sufficient and necessary to learn a near-optimal contract, resolving an open problem on the hardness of online contract design. When contracts are restricted to some subset $\mathcal{F} \subset [0,1]^m$, we define an intrinsic dimension of $\mathcal{F}$ that depends on the covering number of the spherical code in the space and bound the regret in terms of this intrinsic dimension. When $\mathcal{F}$ is the family of linear contracts, the Stackelberg regret grows exactly as $\Theta(T^{2/3})$. The contract design problem is challenging because the utility function is discontinuous. Bounding the discretization error in this setting has been an open problem. In this paper, we identify a limited set of directions in which the utility function is continuous, allowing us to design a new discretization method and bound its error. This approach enables the first upper bound with no restrictions on the contract and action space.

在构建推荐系统时，我们试图向用户输出一套有用的项目。在引擎盖下，排名模型预测两个候选项目中的哪个更好，我们必须将这些成对比较提炼成面向用户的输出。但是，学习的排名模型从来都不是完美的，因此在面值下进行预测并不能保证面向用户的输出是可靠的。通过预先训练的排名模型构建，我们展示了如何返回一组严格保证包含好物品的项目。我们的过程将任何排名模型都具有严格的有限样本控制对错误发现率（FDR）的控制，无论（未知）数据分布如何。此外，我们的校准算法使推荐系统中的多个目标可以简单而原则地集成。例如，我们展示了如何优化通过用户指定水平的FDR控制水平来优化建议多样性，从而规避了指定多样性损失的临时权重，而不是准确的损失。在整个过程中，我们专注于学习对一组可能的建议进行排名的问题，评估我们在Yahoo!上的方法！学习排名和MSMARCO数据集。

Consider the relationship between a regulator (the principal) and a pharmaceutical company (the agent). The pharmaceutical company wishes to sell a product to make a profit, and the FDA wishes to ensure that only efficacious drugs are released to the public. The efficacy of the drug is not known to the FDA, so the pharmaceutical company must run a costly trial to prove efficacy to the FDA. Critically, the statistical protocol used to establish efficacy affects the behavior of a strategic, self-interested pharmaceutical company; a lower standard of statistical evidence incentivizes the pharmaceutical company to run more trials for drugs that are less likely to be effective, since the drug may pass the trial by chance, resulting in large profits. The interaction between the statistical protocol and the incentives of the pharmaceutical company is crucial to understanding this system and designing protocols with high social utility. In this work, we discuss how the principal and agent can enter into a contract with payoffs based on statistical evidence. When there is stronger evidence for the quality of the product, the principal allows the agent to make a larger profit. We show how to design contracts that are robust to an agent's strategic actions, and derive the optimal contract in the presence of strategic behavior.

我们介绍了学习然后测试，校准机器学习模型的框架，使其预测满足明确的，有限样本统计保证，无论底层模型如何和（未知）数据生成分布。框架地址，以及在其他示例中，在多标签分类中的错误发现速率控制，在实例分割中交叉联盟控制，以及同时控制分类或回归中的异常检测和置信度覆盖的类型误差。为实现这一目标，我们解决了一个关键的技术挑战：控制不一定单调的任意风险。我们的主要洞察力是将风险控制问题重新构建为多个假设检测，使技术和数学论据不同于先前文献中的技术。我们使用我们的框架为多个核心机器学习任务提供新的校准方法，在计算机视觉中具有详细的工作示例。

卷积图像分类器可以实现高预测的准确性，但是量化其不确定性仍然是尚未解决的挑战，阻碍了他们在结果环境中的部署。现有的不确定性量化技术（例如PLATT缩放）试图校准网络的概率估计，但它们没有正式的保证。我们提出了一种算法，该算法会修改任何分类器，以输出包含具有用户指定概率的真实标签的预测集，例如90％。该算法像PLATT缩放一样简单快捷，但为每个模型和数据集提供了正式的有限样本覆盖范围保证。我们的方法修改了现有的保形预测算法，从而通过在PLATT缩放后正规化不太可能的类别分数来提供更稳定的预测集。在具有RESNET-152和其他分类器的ImageNet和Imagenet-V2的实验中，我们的方案的表现优于现有方法，通过通常比独立PLATT缩放基线小的5到10个因素实现覆盖范围。

While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels' activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the established right-lateralized Theta sampling network anomaly, in line with the temporal sampling framework's assumption of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively.

This is paper for the smooth function approximation by neural networks (NN). Mathematical or physical functions can be replaced by NN models through regression. In this study, we get NNs that generate highly accurate and highly smooth function, which only comprised of a few weight parameters, through discussing a few topics about regression. First, we reinterpret inside of NNs for regression; consequently, we propose a new activation function--integrated sigmoid linear unit (ISLU). Then special charateristics of metadata for regression, which is different from other data like image or sound, is discussed for improving the performance of neural networks. Finally, the one of a simple hierarchical NN that generate models substituting mathematical function is presented, and the new batch concept ``meta-batch" which improves the performance of NN several times more is introduced. The new activation function, meta-batch method, features of numerical data, meta-augmentation with metaparameters, and a structure of NN generating a compact multi-layer perceptron(MLP) are essential in this study.

We present a novel dataset named as HPointLoc, specially designed for exploring capabilities of visual place recognition in indoor environment and loop detection in simultaneous localization and mapping. The loop detection sub-task is especially relevant when a robot with an on-board RGB-D camera can drive past the same place (``Point") at different angles. The dataset is based on the popular Habitat simulator, in which it is possible to generate photorealistic indoor scenes using both own sensor data and open datasets, such as Matterport3D. To study the main stages of solving the place recognition problem on the HPointLoc dataset, we proposed a new modular approach named as PNTR. It first performs an image retrieval with the Patch-NetVLAD method, then extracts keypoints and matches them using R2D2, LoFTR or SuperPoint with SuperGlue, and finally performs a camera pose optimization step with TEASER++. Such a solution to the place recognition problem has not been previously studied in existing publications. The PNTR approach has shown the best quality metrics on the HPointLoc dataset and has a high potential for real use in localization systems for unmanned vehicles. The proposed dataset and framework are publicly available: https://github.com/metra4ok/HPointLoc.

With growing sophistication and volume of cyber attacks combined with complex network structures, it is becoming extremely difficult for security analysts to corroborate evidences to identify multistage campaigns on their network. This work develops HeAT (Heated Alert Triage): given a critical indicator of compromise (IoC), e.g., a severe IDS alert, HeAT produces a HeATed Attack Campaign (HAC) depicting the multistage activities that led up to the critical event. We define the concept of "Alert Episode Heat" to represent the analysts opinion of how much an event contributes to the attack campaign of the critical IoC given their knowledge of the network and security expertise. Leveraging a network-agnostic feature set, HeAT learns the essence of analyst's assessment of "HeAT" for a small set of IoC's, and applies the learned model to extract insightful attack campaigns for IoC's not seen before, even across networks by transferring what have been learned. We demonstrate the capabilities of HeAT with data collected in Collegiate Penetration Testing Competition (CPTC) and through collaboration with a real-world SOC. We developed HeAT-Gain metrics to demonstrate how analysts may assess and benefit from the extracted attack campaigns in comparison to common practices where IP addresses are used to corroborate evidences. Our results demonstrates the practical uses of HeAT by finding campaigns that span across diverse attack stages, remove a significant volume of irrelevant alerts, and achieve coherency to the analyst's original assessments.