Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and taxi demand prediction, is an important task in deep learning area. However, for the nodes in graph, their ST patterns can vary greatly in difficulties for modeling, owning to the heterogeneous nature of ST data. We argue that unveiling the nodes to the model in a meaningful order, from easy to complex, can provide performance improvements over traditional training procedure. The idea has its root in Curriculum Learning which suggests in the early stage of training models can be sensitive to noise and difficult samples. In this paper, we propose ST-Curriculum Dropout, a novel and easy-to-implement strategy for spatial-temporal graph modeling. Specifically, we evaluate the learning difficulty of each node in high-level feature space and drop those difficult ones out to ensure the model only needs to handle fundamental ST relations at the beginning, before gradually moving to hard ones. Our strategy can be applied to any canonical deep learning architecture without extra trainable parameters, and extensive experiments on a wide range of datasets are conducted to illustrate that, by controlling the difficulty level of ST relations as the training progresses, the model is able to capture better representation of the data and thus yields better generalization.

近年来，保护隐私数据分析已成为普遍存在。在本文中，我们提出了分布式私人多数票投票机制，以解决分布式设置中的标志选择问题。为此，我们将迭代剥离应用于稳定性函数，并使用指数机制恢复符号。作为应用程序，我们研究了分布式系统中的平均估计和线性回归问题的私人标志选择。我们的方法与非私有场景一样，用最佳的信噪比恢复了支持和标志，这比私人变量选择的现代作品要好。此外，符号选择一致性具有理论保证是合理的。进行了模拟研究以证明我们提出的方法的有效性。

估计路径的旅行时间是智能运输系统的重要主题。它是现实世界应用的基础，例如交通监控，路线计划和出租车派遣。但是，为这样的数据驱动任务构建模型需要大量用户的旅行信息，这与其隐私直接相关，因此不太可能共享。数据所有者之间的非独立和相同分布的（非IID）轨迹数据也使一个预测模型变得极具挑战性，如果我们直接应用联合学习。最后，以前关于旅行时间估算的工作并未考虑道路的实时交通状态，我们认为这可以极大地影响预测。为了应对上述挑战，我们为移动用户组引入GOF-TTE，生成的在线联合学习框架以进行旅行时间估计，这是我）使用联合学习方法，允许在培训时将私人数据保存在客户端设备上，并设计设计和设计。所有客户共享的全球模型作为在线生成模型推断实时道路交通状态。 ii）除了在服务器上共享基本模型外，还针对每个客户调整了一个微调的个性化模型来研究其个人驾驶习惯，从而弥补了本地化全球模型预测的残余错误。 ％iii）将全球模型设计为所有客户共享的在线生成模型，以推断实时道路交通状态。我们还对我们的框架采用了简单的隐私攻击，并实施了差异隐私机制，以进一步保证隐私安全。最后，我们对Didi Chengdu和Xi'an的两个现实世界公共出租车数据集进行了实验。实验结果证明了我们提出的框架的有效性。

由于物联网（IoT）技术的快速开发，许多在线Web应用程序（例如Google Map和Uber）估计移动设备收集的轨迹数据的旅行时间。但是，实际上，复杂的因素（例如网络通信和能量限制）使以低采样率收集的多个轨迹。在这种情况下，本文旨在解决稀疏场景中的旅行时间估计问题（TTE）和路线恢复问题，这通常会导致旅行时间的不确定标签以及连续采样的GPS点之间的路线。我们将此问题提出为不进行的监督问题，其中训练数据具有粗糙的标签，并共同解决了TTE和路线恢复的任务。我们认为，这两个任务在模型学习过程中彼此互补并保持这种关系：更精确的旅行时间可以使路由更好地推断，从而导致更准确的时间估计）。基于此假设，我们提出了一种EM算法，以替代E估计通过E步中通过弱监督的推断路线的行进时间，并根据M步骤中的估计行进时间来检索途径，以稀疏轨迹。我们对三个现实世界轨迹数据集进行了实验，并证明了该方法的有效性。

深度估计对于各种重要的现实世界应用至关重要，例如自动驾驶。但是，在高速场景中，它遭受了严重的性能退化，因为传统相机只能捕获模糊的图像。为了解决这个问题，Spike摄像头旨在以高框架速率捕获像素的亮度强度。但是，使用传统的单眼或立体声深度估计算法，使用尖峰摄像机的深度估计仍然非常具有挑战性，这些算法基于光度一致性。在本文中，我们提出了一种新型的不确定性引导深度融合（UGDF）框架，以融合Spike摄像机的单眼和立体声深度估计网络的预测。我们的框架是由于立体声尖峰深度估计在近距离取得更好的结果，而单眼尖峰深度估计获得了更好的结果。因此，我们引入了具有联合培训策略的双任务深度估计结构，并估算了分布式不确定性以融合单眼和立体声结果。为了证明尖峰深度估计比传统的摄像头深度估计的优势，我们为一个名为CitySpike20k的尖峰深度数据集，其中包含20k配对的样品，以进行尖峰深度估计。 UGDF在CitySpike20k上取得了最新的结果，超过了所有单眼或立体声尖峰深度估计基线。我们进行了广泛的实验，以评估我们方法对CitySpike20k的有效性和概括。据我们所知，我们的框架是第一个用于尖峰摄像头深度估算的双任务融合框架。代码和数据集将发布。

聚类是一种代表性的无监督方法，广泛应用于多模式和多视图方案。多个内核聚类（MKC）旨在通过集成基础内核的互补信息来分组数据。作为代表，后期的Fusion MKC首先将内核分解为正交分区矩阵，然后从他们那里学习共识，最近实现了有希望的表现。但是，这些方法无法考虑分区矩阵内部的噪声，从而阻止了聚类性能的进一步改善。我们发现噪声可以分解为可分离的双部分，即n-noise和c-noise（空空间噪声和柱空间噪声）。在本文中，我们严格地定义了双噪声，并通过最小化新颖的无参数MKC算法提出了新颖的MKC算法。为了解决最终的优化问题，我们设计了有效的两步迭代策略。据我们所知，这是第一次研究内核空间中分区中的双重噪声。我们观察到双重噪声会污染对角线结构并产生聚类性能的变性，而C-Noise比N-Noise表现出更大的破坏。由于我们的有效机制可以最大程度地减少双重噪声，因此所提出的算法超过了最新的方法。

We study episodic two-player zero-sum Markov games (MGs) in the offline setting, where the goal is to find an approximate Nash equilibrium (NE) policy pair based on a dataset collected a priori. When the dataset does not have uniform coverage over all policy pairs, finding an approximate NE involves challenges in three aspects: (i) distributional shift between the behavior policy and the optimal policy, (ii) function approximation to handle large state space, and (iii) minimax optimization for equilibrium solving. We propose a pessimism-based algorithm, dubbed as pessimistic minimax value iteration (PMVI), which overcomes the distributional shift by constructing pessimistic estimates of the value functions for both players and outputs a policy pair by solving NEs based on the two value functions. Furthermore, we establish a data-dependent upper bound on the suboptimality which recovers a sublinear rate without the assumption on uniform coverage of the dataset. We also prove an information-theoretical lower bound, which suggests that the data-dependent term in the upper bound is intrinsic. Our theoretical results also highlight a notion of "relative uncertainty", which characterizes the necessary and sufficient condition for achieving sample efficiency in offline MGs. To the best of our knowledge, we provide the first nearly minimax optimal result for offline MGs with function approximation.

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on two datasets with different organs and modalities, where it substantially outperforms existing techniques.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.