In various fields of data science, researchers are often interested in estimating the ratio of conditional expectation functions (CEFR). Specifically in causal inference problems, it is sometimes natural to consider ratio-based treatment effects, such as odds ratios and hazard ratios, and even difference-based treatment effects are identified as CEFR in some empirically relevant settings. This chapter develops the general framework for estimation and inference on CEFR, which allows the use of flexible machine learning for infinite-dimensional nuisance parameters. In the first stage of the framework, the orthogonal signals are constructed using debiased machine learning techniques to mitigate the negative impacts of the regularization bias in the nuisance estimates on the target estimates. The signals are then combined with a novel series estimator tailored for CEFR. We derive the pointwise and uniform asymptotic results for estimation and inference on CEFR, including the validity of the Gaussian bootstrap, and provide low-level sufficient conditions to apply the proposed framework to some specific examples. We demonstrate the finite-sample performance of the series estimator constructed under the proposed framework by numerical simulations. Finally, we apply the proposed method to estimate the causal effect of the 401(k) program on household assets.

Question answering (QA) models for reading comprehension tend to learn shortcut solutions rather than the solutions intended by QA datasets. QA models that have learned shortcut solutions can achieve human-level performance in shortcut examples where shortcuts are valid, but these same behaviors degrade generalization potential on anti-shortcut examples where shortcuts are invalid. Various methods have been proposed to mitigate this problem, but they do not fully take the characteristics of shortcuts themselves into account. We assume that the learnability of shortcuts, i.e., how easy it is to learn a shortcut, is useful to mitigate the problem. Thus, we first examine the learnability of the representative shortcuts on extractive and multiple-choice QA datasets. Behavioral tests using biased training sets reveal that shortcuts that exploit answer positions and word-label correlations are preferentially learned for extractive and multiple-choice QA, respectively. We find that the more learnable a shortcut is, the flatter and deeper the loss landscape is around the shortcut solution in the parameter space. We also find that the availability of the preferred shortcuts tends to make the task easier to perform from an information-theoretic viewpoint. Lastly, we experimentally show that the learnability of shortcuts can be utilized to construct an effective QA training set; the more learnable a shortcut is, the smaller the proportion of anti-shortcut examples required to achieve comparable performance on shortcut and anti-shortcut examples. We claim that the learnability of shortcuts should be considered when designing mitigation methods.

Question answering (QA) models are shown to be insensitive to large perturbations to inputs; that is, they make correct and confident predictions even when given largely perturbed inputs from which humans can not correctly derive answers. In addition, QA models fail to generalize to other domains and adversarial test sets, while humans maintain high accuracy. Based on these observations, we assume that QA models do not use intended features necessary for human reading but rely on spurious features, causing the lack of generalization ability. Therefore, we attempt to answer the question: If the overconfident predictions of QA models for various types of perturbations are penalized, will the out-of-distribution (OOD) generalization be improved? To prevent models from making confident predictions on perturbed inputs, we first follow existing studies and maximize the entropy of the output probability for perturbed inputs. However, we find that QA models trained to be sensitive to a certain perturbation type are often insensitive to unseen types of perturbations. Thus, we simultaneously maximize the entropy for the four perturbation types (i.e., word- and sentence-level shuffling and deletion) to further close the gap between models and humans. Contrary to our expectations, although models become sensitive to the four types of perturbations, we find that the OOD generalization is not improved. Moreover, the OOD generalization is sometimes degraded after entropy maximization. Making unconfident predictions on largely perturbed inputs per se may be beneficial to gaining human trust. However, our negative results suggest that researchers should pay attention to the side effect of entropy maximization.

To ensure the safety of railroad operations, it is important to monitor and forecast track geometry irregularities. A higher safety requires forecasting with a higher spatiotemporal frequency. For forecasting with a high spatiotemporal frequency, it is necessary to capture spatial correlations. Additionally, track geometry irregularities are influenced by multiple exogenous factors. In this study, we propose a method to forecast one type of track geometry irregularity, vertical alignment, by incorporating spatial and exogenous factor calculations. The proposed method embeds exogenous factors and captures spatiotemporal correlations using a convolutional long short-term memory (ConvLSTM). In the experiment, we compared the proposed method with other methods in terms of the forecasting performance. Additionally, we conducted an ablation study on exogenous factors to examine their contribution to the forecasting performance. The results reveal that spatial calculations and maintenance record data improve the forecasting of the vertical alignment.

我们提出了一种使用变异隐式神经表示（INR）的动作条件人类运动产生方法。变分形式主义可以使INR的动作条件分布，从中可以轻松地采样表示形式以产生新的人类运动序列。我们的方法通过构造提供可变的长度序列生成，因为INR的一部分已针对随时间嵌入的整个任意长度进行了优化。相反，以前的作品报告了建模可变长度序列的困难。我们证实，使用变压器解码器的方法优于人类Act12，NTU-RGBD和UESTC数据集的所有相关方法，从现实主义和生成动作的多样性方面。令人惊讶的是，即使我们使用MLP解码器的方法也始终优于最先进的基于变压器的自动编码器。特别是，我们表明，在现实主义和多样性方面，我们方法生成的可变长度运动比最先进方法产生的固定长度运动更好。 https://github.com/pacerv/implicitmotion上的代码。

本研究旨在解决二次多尺寸机器人到执行器故障的容错问题，这对于在远程或极端环境中运行的机器人至关重要。特别地，建立了具有动态随机化（ACDR）的自适应课程增强学习算法。ACDR算法可以在随机执行器故障条件下自适应地培训四足机器人，并制定一个用于容错机器人控制的单一强大策略。值得注意的是，难以使静止的课程比易于2个课程更有效地用于四足机器人机器人。ACDR算法可用于构建机器人系统，该机器人不需要其他模块检测执行器故障和切换策略。实验结果表明，ACDR算法在平均奖励和步行距离方面优于传统算法。

涉及将知识从富含标签的源域传送到未标记的目标域的无监督域适应，可用于大大降低对象检测领域的注释成本。在这项研究中，我们证明了源域的对抗训练可以作为无监督域适应的新方法。具体地，我们建立了普遍训练的探测器在源极域中显着移位的目标域中实现了改进的检测性能。这种现象归因于普遍训练的探测器可用于提取与人类感知的鲁棒特征提取鲁棒特征，并在丢弃特定于域的非鲁棒特征的同时在域中传输域。此外，我们提出了一种结合对抗性训练和特征对准的方法，以确保具有目标域的鲁棒特征的改进对准。我们对四个基准数据集进行实验，并确认我们在大型域转移到艺术图像的大域移位的有效性。与基线模型相比，普遍训练的探测器在结合特征对准时将平均平均精度提高至7.7％，进一步高达11.8％。虽然我们的方法降低了对小型域移位的性能，但基于Frechet距离的域移位的量化允许我们确定是否应该进行抗逆性培训。