城市化及其问题需要对城市动态，尤其是现代城市复杂而多样化的生活方式的深入和全面的了解。数字化的数据可以准确捕获复杂的人类活动，但缺乏人口统计数据的解释性。在本文中，我们研究了美国11个都会区的120万人到110万个地方的出行探访模式的隐私增强数据集，以检测美国最大的美国城市中的潜在行动行为和生活方式。尽管出行访问的复杂性很大，但我们发现生活方式可以自动分解为12种潜在的可解释的活动行为，人们如何将购物，饮食，工作或利用空闲时间结合起来。我们没有描述具有单一生活方式的人，而是发现城市居民的行为是这些行为的混合。那些被检测到的潜在活动行为同样存在于城市之间，无法通过主要人口特征来完全解释。最后，我们发现这些潜在行为与在控制人口特征之后，即使在控制人口特征之后，这些潜在行为也与经验丰富的收入隔离，运输或健康行为有关。我们的结果表明，与活动行为相辅相成，以了解城市动态的重要性。

我们介绍了$ \ pi $ -test，这是一种用于测试跨多方数据分布的数据之间的统计独立性的隐私保护算法。我们的算法依赖于私人估计数据集之间的距离相关性，这是SZ \'ekely等人中引入的独立性的定量度量。[2007]。我们在差异私有测试的实用性上建立了加法和乘法误差界，我们相信在涉及敏感数据的各种分布式假设测试设置中，我们会发现应用程序。

域泛化涉及从异构地收集培训来源的分类器，以便它推广到从类似的未知目标域中汲取的数据，具有大规模学习和个性化推断的应用。在许多设置中，隐私问题禁止获取培训数据样本的域标签，而是只有汇总培训点集合。利用域标签来创建域不变特征表示的现有方法在此设置中不可应用，需要替代方法来学习概括的分类器。在本文中，我们提出了一个解决这个问题的域 - 自适应方法，它分为两个步骤：（a）我们在仔细选择的特征空间内培训数据来创建伪域，（b）使用这些伪域学习域 - 自适应分类器，该分类器使用有关它所属的输入和伪域的信息进行预测。我们的方法在各种域泛化基准测试中实现了最先进的性能，而无需使用域标签。此外，我们使用群集信息提供关于域泛化的新颖理论保障。我们的方法可以适用于基于集合的方法，即使在大型基准数据集上也可以提供大量的收益。代码可以在：https://github.com/xavierohan/adaclust_domainbed

合作匪徒问题越来越多地成为其在大规模决策中的应用。然而，对此问题的大多数研究专注于具有完美通信的环境，而在大多数现实世界分布式设置中，通信通常是随机网络，具有任意损坏和延迟。在本文中，我们在三个典型的真实沟通场景下研究了合作匪徒学习，即（a）通过随机时变网络的消息传递，（b）通过随机延迟的网络瞬时奖励共享（c ）通过对冲损坏的奖励来传递消息，包括拜占庭式沟通。对于每个环境中的每一个，我们提出了实现竞争性能的分散算法，以及在发生的群体后悔的近乎最佳保证。此外，在具有完美通信的环境中，我们提出了一种改进的延迟更新算法，其优于各种网络拓扑的现有最先进的算法。最后，我们在集团后悔呈现紧密的网络依赖性最低限度。我们所提出的算法很简单，以实现和获得竞争性的经验性能。

长期联系，桥梁不同社区的社会关系被广泛认为在社会网络中传播新颖信息中发挥关键作用。然而，一些现有的网络理论和预测模型表明，长圆圈可能会迅速溶解或最终变得多余，从而提出质疑长期长期的长期值。我们对现实世界动态网络的实证分析表明，与这种推理相反，长期关系比其他社会关系更有可能持续存在，而且它们中的许多人在不被嵌入在当地网络而不嵌入社会桥梁时不断起作用。使用新颖的成本效益分析模型与机器学习相结合，我们表明长期关系是非常有益的，这本能地激励人们花费额外的努力来维护它们。这部分解释了为什么长的关系比许多现有理论和模型所建议的更持久性。总体而言，我们的研究表明，需要促进长期关系的社会干预的必要性，例如混合各种背景的人。

We derive a set of causal deep neural networks whose architectures are a consequence of tensor (multilinear) factor analysis. Forward causal questions are addressed with a neural network architecture composed of causal capsules and a tensor transformer. The former estimate a set of latent variables that represent the causal factors, and the latter governs their interaction. Causal capsules and tensor transformers may be implemented using shallow autoencoders, but for a scalable architecture we employ block algebra and derive a deep neural network composed of a hierarchy of autoencoders. An interleaved kernel hierarchy preprocesses the data resulting in a hierarchy of kernel tensor factor models. Inverse causal questions are addressed with a neural network that implements multilinear projection and estimates the causes of effects. As an alternative to aggressive bottleneck dimension reduction or regularized regression that may camouflage an inherently underdetermined inverse problem, we prescribe modeling different aspects of the mechanism of data formation with piecewise tensor models whose multilinear projections are well-defined and produce multiple candidate solutions. Our forward and inverse neural network architectures are suitable for asynchronous parallel computation.

The existing methods for video anomaly detection mostly utilize videos containing identifiable facial and appearance-based features. The use of videos with identifiable faces raises privacy concerns, especially when used in a hospital or community-based setting. Appearance-based features can also be sensitive to pixel-based noise, straining the anomaly detection methods to model the changes in the background and making it difficult to focus on the actions of humans in the foreground. Structural information in the form of skeletons describing the human motion in the videos is privacy-protecting and can overcome some of the problems posed by appearance-based features. In this paper, we present a survey of privacy-protecting deep learning anomaly detection methods using skeletons extracted from videos. We present a novel taxonomy of algorithms based on the various learning approaches. We conclude that skeleton-based approaches for anomaly detection can be a plausible privacy-protecting alternative for video anomaly detection. Lastly, we identify major open research questions and provide guidelines to address them.

Several self-supervised representation learning methods have been proposed for reinforcement learning (RL) with rich observations. For real-world applications of RL, recovering underlying latent states is crucial, particularly when sensory inputs contain irrelevant and exogenous information. In this work, we study how information bottlenecks can be used to construct latent states efficiently in the presence of task-irrelevant information. We propose architectures that utilize variational and discrete information bottlenecks, coined as RepDIB, to learn structured factorized representations. Exploiting the expressiveness bought by factorized representations, we introduce a simple, yet effective, bottleneck that can be integrated with any existing self-supervised objective for RL. We demonstrate this across several online and offline RL benchmarks, along with a real robot arm task, where we find that compressed representations with RepDIB can lead to strong performance improvements, as the learned bottlenecks help predict only the relevant state while ignoring irrelevant information.

We propose reconstruction probing, a new analysis method for contextualized representations based on reconstruction probabilities in masked language models (MLMs). This method relies on comparing the reconstruction probabilities of tokens in a given sequence when conditioned on the representation of a single token that has been fully contextualized and when conditioned on only the decontextualized lexical prior of the model. This comparison can be understood as quantifying the contribution of contextualization towards reconstruction -- the difference in the reconstruction probabilities can only be attributed to the representational change of the single token induced by contextualization. We apply this analysis to three MLMs and find that contextualization boosts reconstructability of tokens that are close to the token being reconstructed in terms of linear and syntactic distance. Furthermore, we extend our analysis to finer-grained decomposition of contextualized representations, and we find that these boosts are largely attributable to static and positional embeddings at the input layer.

Diffusion models have achieved justifiable popularity by attaining state-of-the-art performance in generating realistic objects from seemingly arbitrarily complex data distributions, including when conditioning generation on labels. Unfortunately, however, their iterative nature renders them very computationally inefficient during the sampling process. For the multi-class conditional generation problem, we propose a novel, structurally unique framework of diffusion models which are hierarchically branched according to the inherent relationships between classes. In this work, we demonstrate that branched diffusion models offer major improvements in efficiently generating samples from multiple classes. We also showcase several other advantages of branched diffusion models, including ease of extension to novel classes in a continual-learning setting, and a unique interpretability that offers insight into these generative models. Branched diffusion models represent an alternative paradigm to their traditional linear counterparts, and can have large impacts in how we use diffusion models for efficient generation, online learning, and scientific discovery.