A noisy training set usually leads to the degradation of the generalization and robustness of neural networks. In this paper, we propose a novel theoretically guaranteed clean sample selection framework for learning with noisy labels. Specifically, we first present a Scalable Penalized Regression (SPR) method, to model the linear relation between network features and one-hot labels. In SPR, the clean data are identified by the zero mean-shift parameters solved in the regression model. We theoretically show that SPR can recover clean data under some conditions. Under general scenarios, the conditions may be no longer satisfied; and some noisy data are falsely selected as clean data. To solve this problem, we propose a data-adaptive method for Scalable Penalized Regression with Knockoff filters (Knockoffs-SPR), which is provable to control the False-Selection-Rate (FSR) in the selected clean data. To improve the efficiency, we further present a split algorithm that divides the whole training set into small pieces that can be solved in parallel to make the framework scalable to large datasets. While Knockoffs-SPR can be regarded as a sample selection module for a standard supervised training pipeline, we further combine it with a semi-supervised algorithm to exploit the support of noisy data as unlabeled data. Experimental results on several benchmark datasets and real-world noisy datasets show the effectiveness of our framework and validate the theoretical results of Knockoffs-SPR. Our code and pre-trained models will be released.

The task of Few-shot learning (FSL) aims to transfer the knowledge learned from base categories with sufficient labelled data to novel categories with scarce known information. It is currently an important research question and has great practical values in the real-world applications. Despite extensive previous efforts are made on few-shot learning tasks, we emphasize that most existing methods did not take into account the distributional shift caused by sample selection bias in the FSL scenario. Such a selection bias can induce spurious correlation between the semantic causal features, that are causally and semantically related to the class label, and the other non-causal features. Critically, the former ones should be invariant across changes in distributions, highly related to the classes of interest, and thus well generalizable to novel classes, while the latter ones are not stable to changes in the distribution. To resolve this problem, we propose a novel data augmentation strategy dubbed as PatchMix that can break this spurious dependency by replacing the patch-level information and supervision of the query images with random gallery images from different classes from the query ones. We theoretically show that such an augmentation mechanism, different from existing ones, is able to identify the causal features. To further make these features to be discriminative enough for classification, we propose Correlation-guided Reconstruction (CGR) and Hardness-Aware module for instance discrimination and easier discrimination between similar classes. Moreover, such a framework can be adapted to the unsupervised FSL scenario.

时间序列分类是现实世界中的重要问题。由于其非平稳属性随着时间的推移而变化，因此建立泛化模型以表现出来的分布仍然具有挑战性。在本文中，我们建议从分布的角度查看时间序列分类问题。我们认为时间复杂性归因于其中未知的潜在分布。为此，我们建议多元化学习时间序列分类的广义表示。多元化进行了一个迭代过程：它首先通过对抗训练获得了最坏情况的分布场景，然后与获得的子域的分布匹配。我们还提供了一些理论见解。我们进行有关手势识别，语音命令识别，可穿戴压力和影响检测的实验，以及基于传感器的人类活动识别，在不同的情况下总共有七个数据集。结果表明，多样化的多样化大大优于其他基线，并通过定性和定量分析有效地表征了潜在分布。

在我们最近在加纳被动饮食监测的饮食评估现场研究中，我们收集了超过25万件野外图像。该数据集是一种持续的努力，旨在通过被动监控摄像头技术在低收入和中等收入国家中准确测量单个食物和营养摄入量。目前的数据集涉及加纳农村地区和城市地区的20个家庭（74个受试者），研究中使用了两种不同类型的可穿戴摄像机。一旦开始，可穿戴摄像机会不断捕获受试者的活动，该活动会产生大量的数据，以便在进行分析之前清洁和注释。为了简化数据后处理和注释任务，我们提出了一个新颖的自学学习框架，以将大量以自我为中心的图像聚集到单独的事件中。每个事件都由一系列时间连续和上下文相似的图像组成。通过将图像聚集到单独的事件中，注释者和营养师可以更有效地检查和分析数据，并促进随后的饮食评估过程。在带有地面真实标签的固定测试套装上验证，拟议的框架在聚集质量和分类准确性方面优于基准。

在现代电力系统中，关于发电/消耗的实时数据及其相关特征存储在各种分布式方中，包括家用仪表，变压器站和外部组织。为了充分利用这些分布式数据的潜在模式，以进行准确的功率预测，需要联合学习作为协作但隐私保留培训方案。然而，目前的联合学习框架偏振朝向解决数据的水平或垂直分离，并且倾向于忽略两个存在的情况。此外，在主流级联联合学习框架中，仅采用人工神经网络来学习数据模式，与表格数据集的基于树的模型相比，该数据模式被认为是更准确和解释的。为此，我们提出了一种基于XGBoost的混合联合学习框架，用于从实时外部功能的分布式电源预测。除了引入提升的树木以提高准确性和可解释性之外，我们还结合了水平和垂直的联邦学习，以解决特征在当地异构各方分散的场景，并在各种当地地区分散样品。此外，我们设计了动态任务分配方案，使得各方获得公平的信息份额，并且每个方的计算能力可以充分利用促进培训效率。提出了一个后续案例研究，以证明采用拟议框架的必要性。还确认了拟议框架的优点，效率和精度性能。

我们建议在监督回归方案中学习一个不变的因果预测因子，该预测因素对分布变化是可靠的。基于描述潜在数据生成过程的分离的因果分解，我们将分布转移归因于生成因子的突变，该突变涵盖了各种分布转移的情况，因为我们没有在因果结构或因果结构的来源或源突变。在此因果框架下，我们根据操作员确定一组不变预测变量。我们提供了足够的必要条件，使预测变量是最佳最佳的预测因子，即最大程度地减少所有领域中最坏的二次二次损失。在马尔可夫人和忠诚的假设下，这种情况是合理的，因此启发了一种实用算法以识别最佳预测指标。对于经验估计，我们提出了一个以当地因果发现程序为指导的置换式恢复计划。我们方法的效用和有效性在模拟数据和两个现实世界中得到了证明：阿尔茨海默氏病的诊断和基因功能预测。

随着智能传感器的部署和通信技术的进步，大数据分析在智能电网域中大大流行，告知利益相关者最好的电力利用策略。但是，这些电源相关数据被不同的各方存储和拥有。例如，功耗数据存储在跨城市的众多变压器站中;移动公司持有的人口的流动性数据，这是耗电量重要指标。直接数据分享可能会妥协党的福利，个人隐私甚至国家安全。灵感来自谷歌AI的联邦学习计划，我们向智能电网提出了联合学习框架，这使得能够协作学习功耗模式而不会泄漏各个电力迹线。当数据分散在样本空间中时，采用横向联合学习;另一方面，垂直联合学习是为散射在特征空间中的数据的情况而设计的。案例研究表明，通过适当的加密方案，如Paillier加密，从提出的框架构建的机器学习模型是无损，隐私保留和有效的。最后，讨论了智能电网其他方面的联合学习的有希望的未来，包括电动车辆，分布式发电/消费和集成能量系统。

传统的监督学习方法，尤其是深的学习方法，发现对分发超出（OOD）示例敏感，主要是因为所学习的表示与由于其域特异性相关性的变异因子混合了语义因素，而只有语义因子导致输出。为了解决这个问题，我们提出了一种基于因果推理的因果语义生成模型（CSG），以便分别建模两个因素，以及从单个训练域中的oo ood预测的制定方法，这是常见和挑战的。该方法基于因果不变原理，在变形贝斯中具有新颖的设计，用于高效学习和易于预测。从理论上讲，我们证明，在某些条件下，CSG可以通过拟合训练数据来识别语义因素，并且这种语义识别保证了泛化概率的界限和适应的成功。实证研究表明，改善了卓越的基线表现。

Improving the visual quality of the given degraded observation by correcting exposure level is a fundamental task in the computer vision community. Existing works commonly lack adaptability towards unknown scenes because of the data-driven patterns (deep networks) and limited regularization (traditional optimization), and they usually need time-consuming inference. These two points heavily limit their practicability. In this paper, we establish a Practical Exposure Corrector (PEC) that assembles the characteristics of efficiency and performance. To be concrete, we rethink the exposure correction to provide a linear solution with exposure-sensitive compensation. Around generating the compensation, we introduce an exposure adversarial function as the key engine to fully extract valuable information from the observation. By applying the defined function, we construct a segmented shrinkage iterative scheme to generate the desired compensation. Its shrinkage nature supplies powerful support for algorithmic stability and robustness. Extensive experimental evaluations fully reveal the superiority of our proposed PEC. The code is available at https://rsliu.tech/PEC.

Developing autonomous vehicles (AVs) helps improve the road safety and traffic efficiency of intelligent transportation systems (ITS). Accurately predicting the trajectories of traffic participants is essential to the decision-making and motion planning of AVs in interactive scenarios. Recently, learning-based trajectory predictors have shown state-of-the-art performance in highway or urban areas. However, most existing learning-based models trained with fixed datasets may perform poorly in continuously changing scenarios. Specifically, they may not perform well in learned scenarios after learning the new one. This phenomenon is called "catastrophic forgetting". Few studies investigate trajectory predictions in continuous scenarios, where catastrophic forgetting may happen. To handle this problem, first, a novel continual learning (CL) approach for vehicle trajectory prediction is proposed in this paper. Then, inspired by brain science, a dynamic memory mechanism is developed by utilizing the measurement of traffic divergence between scenarios, which balances the performance and training efficiency of the proposed CL approach. Finally, datasets collected from different locations are used to design continual training and testing methods in experiments. Experimental results show that the proposed approach achieves consistently high prediction accuracy in continuous scenarios without re-training, which mitigates catastrophic forgetting compared to non-CL approaches. The implementation of the proposed approach is publicly available at https://github.com/BIT-Jack/D-GSM