Factorization machines (FMs) are a powerful tool for regression and classification in the context of sparse observations, that has been successfully applied to collaborative filtering, especially when side information over users or items is available. Bayesian formulations of FMs have been proposed to provide confidence intervals over the predictions made by the model, however they usually involve Markov-chain Monte Carlo methods that require many samples to provide accurate predictions, resulting in slow training in the context of large-scale data. In this paper, we propose a variational formulation of factorization machines that allows us to derive a simple objective that can be easily optimized using standard mini-batch stochastic gradient descent, making it amenable to large-scale data. Our algorithm learns an approximate posterior distribution over the user and item parameters, which leads to confidence intervals over the predictions. We show, using several datasets, that it has comparable or better performance than existing methods in terms of prediction accuracy, and provide some applications in active learning strategies, e.g., preference elicitation techniques.

Offline reinforcement learning (RL) have received rising interest due to its appealing data efficiency. The present study addresses behavior estimation, a task that lays the foundation of many offline RL algorithms. Behavior estimation aims at estimating the policy with which training data are generated. In particular, this work considers a scenario where the data are collected from multiple sources. In this case, neglecting data heterogeneity, existing approaches for behavior estimation suffers from behavior misspecification. To overcome this drawback, the present study proposes a latent variable model to infer a set of policies from data, which allows an agent to use as behavior policy the policy that best describes a particular trajectory. This model provides with a agent fine-grained characterization for multi-source data and helps it overcome behavior misspecification. This work also proposes a learning algorithm for this model and illustrates its practical usage via extending an existing offline RL algorithm. Lastly, with extensive evaluation this work confirms the existence of behavior misspecification and the efficacy of the proposed model.

研究过程包括许多决定，例如如何应有资格以及在何处发表论文。在本文中，我们介绍了一个一般框架，以调查此类决策的影响。研究效果的主要困难是我们需要了解反事实结果，而实际上并非现实。我们框架的主要见解是灵感来自现有的反事实分析，其中研究人员将双胞胎视为反事实单位。提出的框架将一对彼此引用为双胞胎的论文。这些论文往往是平行的作品，在类似的主题和类似社区中。我们调查了采用不同决策的双论文，观察这些研究带来的研究影响的进展，并通过这些研究的影响来估算决策的影响。我们发布了我们的代码和数据，我们认为由于数据集缺乏反事实研究，因此这是非常有益的。

找到与治疗效果差异相关的特征对于揭示基本因果机制至关重要。现有方法通过测量特征属性如何影响{\ iT条件平均治疗效果}（CATE）的程度来寻求此类特征。但是，这些方法可能会忽略重要特征，因为CATE是平均治疗效果的度量，无法检测到平均值以外的其他分布参数（例如方差）的差异。为了解决现有方法的这种弱点，我们提出了一个特征选择框架，以发现{\ IT分布处理效果修饰符}。我们首先制定特征重要性度量，该指标量化特征属性如何影响潜在结果分布之间的差异。然后，我们得出其计算高效的估计器，并开发了一个功能选择算法，该算法可以将I型错误率控制为所需级别。实验结果表明，我们的框架成功地发现了重要特征，并优于现有的基于均值的方法。

批量增强学习的缺点是其对数据奖励的要求，因此不适用于无需奖励功能的任务。缺乏奖励的现有设置，如行为克隆，依靠从人类收集的最佳示威。不幸的是，确保最优性需要广泛的专业知识，这阻碍了复杂任务的大规模数据。本文通过从偏好学习奖励功能来解决批量增强学习环境中缺乏奖励。生成偏好只需要对任务的基本了解。作为心理过程，生成偏好比执行演示更快。因此，可以使用众包从非专家人类的规模收集偏好。本文在收集来自非专家人类的数据时出现的危急挑战：偏好中的噪音。提出了一种用于建模标签可靠性的新型概率模型，其利用标签协作。此外，所提出的模型将估计与学习奖励功能平滑。 Atari Datasets的评估展示了拟议模型的有效性，其次是一项消融研究，分析所提出的想法的相对重要性。

“移动”一词的距离（WMD）是测量两个文档相似性的基本技术。作为WMD的关键，它可以通过采用最佳传输配方来利用空间单词的基础几何形状。关于WMD的最初研究报告说，WMD在各种数据集中的大幅度边缘优于古典基线，例如词袋（Bow）和TF-IDF。在本文中，我们指出原始研究中的评估可能会产生误导。我们重新评估了WMD和经典基准的性能，并发现如果我们采用适当的预处理（即L1归一化），经典的基线与WMD具有竞争力。此外，我们引入了WMD和L1拟态化的弓之间的类比，发现不仅WMD的性能，而且距离值都类似于高维空间的弓形值。

选择学术论文的出版物场所是研究过程中的关键一步。但是，在许多情况下，决策仅基于研究人员的经验，这通常会导致次优结果。尽管存在用于学术论文的场地推荐系统，但他们推荐了预计将发表该论文的场所。在这项研究中，我们的目标是从不同的角度推荐出版场所。我们估计，如果在每个场所发表论文，并推荐该论文具有最大潜在影响的场地，则将收到的引用数量。但是，这项任务面临两个挑战。首先，仅在一个地点发表论文，因此，如果该论文发表在另一个地点，我们无法观察到该论文收到的引用数量。其次，论文和出版物场所的内容在统计上是不独立的。也就是说，选择出版物场所存在选择偏见。在本文中，我们将场地推荐问题作为治疗效果估计问题提出。我们使用偏见校正方法来估计有效选择出版物场地的潜在影响，并根据每个场所的论文的潜在影响推荐场地。我们使用计算机科学会议的纸质数据强调了我们方法的有效性。

预测化合物的化学性质在发现具有具体所需特征的新型材料和药物方面至关重要。最近机器学习技术的显着进展使得能够从文献中报告的过去的实验数据启用自动预测建模。然而，这些数据集通常被偏置，因为各种原因，例如实验计划和出版物决策，并且使用这种偏置数据集训练的预测模型经常遭受对偏置分布的过度拟合，并且在随后的用途时执行不良。因此，本研究专注于减轻实验数据集中的偏差。我们采用了两种来自因果推断和域适应的技术与图形神经网络相结合，可以代表分子结构。在四种可能的偏置方案中的实验结果表明，基于逆倾向评分的方法使得稳定的改进，但是域不变的表示学习方法失败。

This study demonstrates the feasibility of point cloud-based proactive link quality prediction for millimeter-wave (mmWave) communications. Image-based methods to quantitatively and deterministically predict future received signal strength using machine learning from time series of depth images to mitigate the human body line-of-sight (LOS) path blockage in mmWave communications have been proposed. However, image-based methods have been limited in applicable environments because camera images may contain private information. Thus, this study demonstrates the feasibility of using point clouds obtained from light detection and ranging (LiDAR) for the mmWave link quality prediction. Point clouds represent three-dimensional (3D) spaces as a set of points and are sparser and less likely to contain sensitive information than camera images. Additionally, point clouds provide 3D position and motion information, which is necessary for understanding the radio propagation environment involving pedestrians. This study designs the mmWave link quality prediction method and conducts two experimental evaluations using different types of point clouds obtained from LiDAR and depth cameras, as well as different numerical indicators of link quality, received signal strength and throughput. Based on these experiments, our proposed method can predict future large attenuation of mmWave link quality due to LOS blockage by human bodies, therefore our point cloud-based method can be an alternative to image-based methods.

The demand for resilient logistics networks has increased because of recent disasters. When we consider optimization problems, entropy regularization is a powerful tool for the diversification of a solution. In this study, we proposed a method for designing a resilient logistics network based on entropy regularization. Moreover, we proposed a method for analytical resilience criteria to reduce the ambiguity of resilience. First, we modeled the logistics network, including factories, distribution bases, and sales outlets in an efficient framework using entropy regularization. Next, we formulated a resilience criterion based on probabilistic cost and Kullback--Leibler divergence. Finally, our method was performed using a simple logistics network, and the resilience of the three logistics plans designed by entropy regularization was demonstrated.