Domain adaptation aims to transfer the knowledge acquired by models trained on (data-rich) source domains to (low-resource) target domains, for which a popular method is invariant representation learning. While they have been studied extensively for classification and regression problems, how they apply to ranking problems, where the data and metrics have a list structure, is not well understood. Theoretically, we establish a domain adaptation generalization bound for ranking under listwise metrics such as MRR and NDCG. The bound suggests an adaptation method via learning list-level domain-invariant feature representations, whose benefits are empirically demonstrated by unsupervised domain adaptation experiments on real-world ranking tasks, including passage reranking. A key message is that for domain adaptation, the representations should be analyzed at the same level at which the metric is computed, as we show that learning invariant representations at the list level is most effective for adaptation on ranking problems.

Multivariate time series forecasting constitutes important functionality in cyber-physical systems, whose prediction accuracy can be improved significantly by capturing temporal and multivariate correlations among multiple time series. State-of-the-art deep learning methods fail to construct models for full time series because model complexity grows exponentially with time series length. Rather, these methods construct local temporal and multivariate correlations within subsequences, but fail to capture correlations among subsequences, which significantly affect their forecasting accuracy. To capture the temporal and multivariate correlations among subsequences, we design a pattern discovery model, that constructs correlations via diverse pattern functions. While the traditional pattern discovery method uses shared and fixed pattern functions that ignore the diversity across time series. We propose a novel pattern discovery method that can automatically capture diverse and complex time series patterns. We also propose a learnable correlation matrix, that enables the model to capture distinct correlations among multiple time series. Extensive experiments show that our model achieves state-of-the-art prediction accuracy.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

We study the problem of learning online packing skills for irregular 3D shapes, which is arguably the most challenging setting of bin packing problems. The goal is to consecutively move a sequence of 3D objects with arbitrary shapes into a designated container with only partial observations of the object sequence. Meanwhile, we take physical realizability into account, involving physics dynamics and constraints of a placement. The packing policy should understand the 3D geometry of the object to be packed and make effective decisions to accommodate it in the container in a physically realizable way. We propose a Reinforcement Learning (RL) pipeline to learn the policy. The complex irregular geometry and imperfect object placement together lead to huge solution space. Direct training in such space is prohibitively data intensive. We instead propose a theoretically-provable method for candidate action generation to reduce the action space of RL and the learning burden. A parameterized policy is then learned to select the best placement from the candidates. Equipped with an efficient method of asynchronous RL acceleration and a data preparation process of simulation-ready training sequences, a mature packing policy can be trained in a physics-based environment within 48 hours. Through extensive evaluation on a variety of real-life shape datasets and comparisons with state-of-the-art baselines, we demonstrate that our method outperforms the best-performing baseline on all datasets by at least 12.8% in terms of packing utility.

6G is envisioned to offer higher data rate, improved reliability, ubiquitous AI services, and support massive scale of connected devices. As a consequence, 6G will be much more complex than its predecessors. The growth of the system scale and complexity as well as the coexistence with the legacy networks and the diversified service requirements will inevitably incur huge maintenance cost and efforts for future 6G networks. Network Root Cause Analysis (Net-RCA) plays a critical role in identifying root causes of network faults. In this article, we first give an introduction about the envisioned 6G networks. Next, we discuss the challenges and potential solutions of 6G network operation and management, and comprehensively survey existing RCA methods. Then we propose an artificial intelligence (AI)-empowered Net-RCA framework for 6G. Performance comparisons on both synthetic and real-world network data are carried out to demonstrate that the proposed method outperforms the existing method considerably.

离线增强学习吸引了人们对解决传统强化学习的应用挑战的极大兴趣。离线增强学习使用先前收集的数据集来训练代理而无需任何互动。为了解决对OOD的高估（分布式）动作的高估，保守的估计值对所有输入都具有较低的价值。以前的保守估计方法通常很难避免OOD作用对Q值估计的影响。此外，这些算法通常需要失去一些计算效率，以实现保守估计的目的。在本文中，我们提出了一种简单的保守估计方法，即双重保守估计（DCE），该方法使用两种保守估计方法来限制政策。我们的算法引入了V功能，以避免分发作用的错误，同时隐含得出保守的估计。此外，我们的算法使用可控的罚款术语，改变了培训中保守主义的程度。从理论上讲，我们说明了该方法如何影响OOD动作和分布动作的估计。我们的实验分别表明，两种保守的估计方法影响了所有国家行动的估计。 DCE展示了D4RL的最新性能。

深度学习模型已在大规模视频基准测试上取得了出色的识别结果。但是，当应用于稀有场景或物体的视频时，它们的性能很差，这主要是由于现有视频数据集的偏见。我们从两个不同的角度解决了这个问题：算法和数据集。从算法的角度来看，我们提出了空间感知的多种偏见（SMAD），它既将明确的偏见都与多种相对的对抗性训练和隐含的偏见以及与空间行动重新重量的模块相结合，从行动方面。为了消除内在的数据集偏差，我们建议OmnideBias有选择地利用Web数据进行联合培训，这可以通过更少的Web数据实现更高的性能。为了验证有效性，我们建立评估协议并对现有数据集的重新分配分配和新的评估数据集进行广泛的实验，该数据集的重点是稀有场景。我们还表明，当转移到其他数据集和任务时，辩护形式可以更好地概括。

图形上的神经扩散是一类新型的图形神经网络，最近引起了越来越多的关注。图形神经偏微分方程（PDE）的能力在解决图形神经网络（GNN）的常见障碍方面的能力，例如过度平滑和瓶颈的问题，但尚未对其对逆性攻击的稳健性。在这项工作中，我们探讨了图神经PDE的稳健性。我们从经验上证明，与其他GNN相比，图形神经PDE在本质上对拓扑扰动更为强大。我们通过利用在图形拓扑扰动下利用热半群的稳定性来提供对这一现象的见解。我们讨论了各种图扩散操作员，并将它们与现有的图神经PDE相关联。此外，我们提出了一个一般图形神经PDE框架，可以通过该框架来定义新的强大GNN。我们验证了新模型在多个基准数据集上实现了可比的最新性能。

最近，由于受监督人员重新识别（REID）的表现不佳，域名概括（DG）人REID引起了很多关注，旨在学习一个不敏感的模型，并可以抵抗域的影响偏见。在本文中，我们首先通过实验验证样式因素是域偏差的重要组成部分。基于这个结论，我们提出了一种样式变量且无关紧要的学习方法（SVIL）方法，以消除样式因素对模型的影响。具体来说，我们在SVIL中设计了样式的抖动模块（SJM）。 SJM模块可以丰富特定源域的样式多样性，并减少各种源域的样式差异。这导致该模型重点关注与身份相关的信息，并对样式变化不敏感。此外，我们将SJM模块与元学习算法有机结合，从而最大程度地提高了好处并进一步提高模型的概括能力。请注意，我们的SJM模块是插件和推理，无需成本。广泛的实验证实了我们的SVIL的有效性，而我们的方法的表现优于DG-REID基准测试的最先进方法。

社会过程的持续数字化转化为时间序列数据的扩散，这些数据涵盖了诸如欺诈检测，入侵检测和能量管理等应用，在这种应用程序中，异常检测通常对于启用可靠性和安全性至关重要。许多最近的研究针对时间序列数据的异常检测。实际上，时间序列异常检测的特征是不同的数据，方法和评估策略，现有研究中的比较仅考虑了这种多样性的一部分，这使得很难为特定问题设置选择最佳方法。为了解决这一缺点，我们介绍了有关数据，方法和评估策略的分类法，并使用分类法提供了无监督时间序列检测的全面概述，并系统地评估和比较了最先进的传统以及深度学习技术。在使用九个公开可用数据集的实证研究中，我们将最常用的性能评估指标应用于公平实施标准下的典型方法。根据分类法提供的结构化，我们报告了经验研究，并以比较表的形式提供指南，以选择最适合特定应用程序设置的方法。最后，我们为这个动态领域提出了研究方向。