本文提出了概率共形预测（PCP），这是一种预测推理算法，该算法通过不连续的预测集估算目标变量。给定输入，PCP基于估计生成模型的随机样品构建预测集。它有效且与显式或隐式有条件生成模型兼容。从理论上讲，我们表明PCP可以保证使用有限样品正确的边际覆盖范围。从经验上讲，我们研究了PCP在各种模拟和真实数据集上。与现有的共形推断方法相比，PCP提供了更清晰的预测集。

与传统的监督学习不同，在许多设置中，只有部分反馈。我们只能遵守所选行动的结果，但不是与其他替代方案相关的反事成员。此类环境包括各种应用，包括定价，在线营销和精密药。关键挑战是观察数据受到系统部署的历史政策的影响，产生了偏置数据分布。我们将此任务作为域适应问题，提出了一种自我训练算法，其在观察数据中为有限的未经验证行动的分类值释放结果，以模拟通过伪标记的随机试验，我们称之为反事实自我训练（CST） 。 CST迭代地赋予伪标签并检测模型。此外，我们显示输入一致性损失可以进一步提高CST性能，这是近伪标签的理论分析中所示的。我们展示了合成和实时数据集在合成和实际数据集上的提出算法的有效性。

我们研究了一个定价设置，其中每个客户都基于客户和/或产品特征提供了一种预测客户对该产品的估值的产品特征。通常只有历史销售记录，我们遵守每个客户是否以规定的价格购买产品，而不是客户的真实估值。因此，数据受到历史销售政策的影响，历史销售政策在没有进行实际实验的可能性的情况下估算未来损失/遗憾的困难/遗憾的损失/遗憾，而是优化诸如收入管理等下游任务的新政策。我们研究如何制定损失功能，该功能可用于直接优化定价策略，而不是通过中间需求估计阶段，这可能在实践中被偏见，因为模型拼写，正常化或校准差。虽然在估值数据可用时提出了现有方法，但我们提出了观察数据设置的损失函数。为实现这一目标，我们将机器学习的想法适应损坏的标签，我们可以考虑每个观察到的客户的结果（购买或不按规定的价格购买），作为客户估值的（已知）概率转变。从这种转变，我们派生了一类合适的无偏损失功能。在此类中，我们识别最小方差估计器，那些对不良需求函数估计的稳健性，并在估计的需求功能有用时提供指导。此外，我们还表明，当应用于我们的上下文定价环境时，在违规评估文学中流行的估计人员在这类损失职能范围内，并且当每个估算师在实践中可能表现良好时，还提供管理层。

我们研究了给定因果模型的公平约束的最佳臂识别问题。目标是在给定节点上找到软干预，以通过仅通过因果模型的部分知识来满足公平约束的同时最大化结果。问题是通过确保在线市场的公平性的动机。我们提供了对误差概率的理论保证，并经验与两级基线进行算法的效果。

有条件的生成对抗网络（CGANs）将标准无条件GaN框架扩展到学习样本的联合数据标签分布，并已建立为能够产生高保真图像的强大生成模型。这种模型的训练挑战在于将课程信息恰当地注入到其发电机和鉴别器中。对于鉴别器，可以通过（1）直接将标签作为输入或（2）涉及辅助分类损失的标签来实现类调节。在本文中，我们表明前者直接对齐类条件的假和实际数据分布$ p（\ text {image} | \ text {class}）$（{\ EM数据匹配}），而后者对齐数据调节类分布$ p（\ text {class} | \ text {image}）$（{\ EM标签匹配}）。虽然类别可分离性并不直接转化为样本质量，并且如果分类本身是本质上困难的话，如果不同类别的特征映射到同一点，则不能为发电机提供有用的指导，因此可以为同一点映射并因此变得不可分割。通过这种直觉激励，我们提出了一种双重投影GaN（P2Gan）模型，它学会在{\ EM数据匹配}和{\ EM标签匹配}之间平衡。然后，我们提出了一种改进的Cgan模型，通过辅助分类，通过最大限度地减少$ F $ -divergence，通过辅助分类直接对准假和实际条件$ p（\ text {class} | \ text {image}）$。高斯（MOG）数据集的合成混合物和各种现实世界数据集的实验，包括CIFAR100，ImageNet和Vggface2，证明了我们所提出的模型的功效。

In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables. We formalize this scenario by building a new Chinese benchmark KnowSQL consisting of domain-specific questions covering various domains. We then address this problem by presenting formulaic knowledge, rather than by annotating additional data examples. More concretely, we construct a formulaic knowledge bank as a domain knowledge base and propose a framework (ReGrouP) to leverage this formulaic knowledge during parsing. Experiments using ReGrouP demonstrate a significant 28.2% improvement overall on KnowSQL.

Weakly-supervised object localization aims to indicate the category as well as the scope of an object in an image given only the image-level labels. Most of the existing works are based on Class Activation Mapping (CAM) and endeavor to enlarge the discriminative area inside the activation map to perceive the whole object, yet ignore the co-occurrence confounder of the object and context (e.g., fish and water), which makes the model inspection hard to distinguish object boundaries. Besides, the use of CAM also brings a dilemma problem that the classification and localization always suffer from a performance gap and can not reach their highest accuracy simultaneously. In this paper, we propose a casual knowledge distillation method, dubbed KD-CI-CAM, to address these two under-explored issues in one go. More specifically, we tackle the co-occurrence context confounder problem via causal intervention (CI), which explores the causalities among image features, contexts, and categories to eliminate the biased object-context entanglement in the class activation maps. Based on the de-biased object feature, we additionally propose a multi-teacher causal distillation framework to balance the absorption of classification knowledge and localization knowledge during model training. Extensive experiments on several benchmarks demonstrate the effectiveness of KD-CI-CAM in learning clear object boundaries from confounding contexts and addressing the dilemma problem between classification and localization performance.

Dynamic treatment regimes assign personalized treatments to patients sequentially over time based on their baseline information and time-varying covariates. In mobile health applications, these covariates are typically collected at different frequencies over a long time horizon. In this paper, we propose a deep spectral Q-learning algorithm, which integrates principal component analysis (PCA) with deep Q-learning to handle the mixed frequency data. In theory, we prove that the mean return under the estimated optimal policy converges to that under the optimal one and establish its rate of convergence. The usefulness of our proposal is further illustrated via simulations and an application to a diabetes dataset.

Nowadays, time-stamped web documents related to a general news query floods spread throughout the Internet, and timeline summarization targets concisely summarizing the evolution trajectory of events along the timeline. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, in this paper, we propose a Unified Timeline Summarizer (UTS) that can generate abstractive and extractive timeline summaries in time order. Concretely, in the encoder part, we propose a graph-based event encoder that relates multiple events according to their content dependency and learns a global representation of each event. In the decoder part, to ensure the chronological order of the abstractive summary, we propose to extract the feature of event-level attention in its generation process with sequential information remained and use it to simulate the evolutionary attention of the ground truth summary. The event-level attention can also be used to assist in extracting summary, where the extracted summary also comes in time sequence. We augment the previous Chinese large-scale timeline summarization dataset and collect a new English timeline dataset. Extensive experiments conducted on these datasets and on the out-of-domain Timeline 17 dataset show that UTS achieves state-of-the-art performance in terms of both automatic and human evaluations.

Hybrid unmanned aerial vehicles (UAVs) integrate the efficient forward flight of fixed-wing and vertical takeoff and landing (VTOL) capabilities of multicopter UAVs. This paper presents the modeling, control and simulation of a new type of hybrid micro-small UAVs, coined as lifting-wing quadcopters. The airframe orientation of the lifting wing needs to tilt a specific angle often within $ 45$ degrees, neither nearly $ 90$ nor approximately $ 0$ degrees. Compared with some convertiplane and tail-sitter UAVs, the lifting-wing quadcopter has a highly reliable structure, robust wind resistance, low cruise speed and reliable transition flight, making it potential to work fully-autonomous outdoor or some confined airspace indoor. In the modeling part, forces and moments generated by both lifting wing and rotors are considered. Based on the established model, a unified controller for the full flight phase is designed. The controller has the capability of uniformly treating the hovering and forward flight, and enables a continuous transition between two modes, depending on the velocity command. What is more, by taking rotor thrust and aerodynamic force under consideration simultaneously, a control allocation based on optimization is utilized to realize cooperative control for energy saving. Finally, comprehensive Hardware-In-the-Loop (HIL) simulations are performed to verify the advantages of the designed aircraft and the proposed controller.