大型人口系统的分析和控制对研究和工程的各个领域引起了极大的兴趣，从机器人群的流行病学到经济学和金融。一种越来越流行和有效的方法来实现多代理系统中的顺序决策，这是通过多机构增强学习，因为它允许对高度复杂的系统进行自动和无模型的分析。但是，可伸缩性的关键问题使控制和增强学习算法的设计变得复杂，尤其是在具有大量代理的系统中。尽管强化学习在许多情况下都发现了经验成功，但许多代理商的问题很快就变得棘手了，需要特别考虑。在这项调查中，我们将阐明当前的方法，以通过多代理强化学习以及通过诸如平均场游戏，集体智能或复杂的网络理论等研究领域进行仔细理解和分析大型人口系统。这些经典独立的主题领域提供了多种理解或建模大型人口系统的方法，这可能非常适合将来的可拖动MARL算法制定。最后，我们调查了大规模控制的潜在应用领域，并确定了实用系统中学习算法的富有成果的未来应用。我们希望我们的调查可以为理论和应用科学的初级和高级研究人员提供洞察力和未来的方向。

我们考虑在完全合作的多代理系统中进行沟通，在该系统中，代理人对环境有部分观察，并且必须共同采取行动以最大程度地提高整体奖励。我们有一个离散的时间排队网络，在该网络中，代理数据包仅基于当前队列长度的部分信息来排队。队列的缓冲区容量有限，因此当数据包被发送到完整队列时会发生掉落。在这项工作中，我们实施了一个通信渠道，使代理商共享其信息以降低数据包下降率。为了有效的信息共享，我们使用一个名为ATVC的基于注意的通信模型，以选择其他代理的信息信息。然后，代理使用变异自动编码器，VAE和Experts，POE，模型的组合来推断队列状态。最终，代理商学习了他们需要进行的交流以及与谁进行交流，而不是一直与所有人进行交流。我们还从经验上表明，ATVC能够推断出队列的真实状态，并导致一项胜过现有基准的政策。

近年来，数据中心和云服务的容量和并行处理能力大大提高。为了充分利用所述分布式系统，必须实现并行排队架构的最佳负载平衡。现有的最新解决方案未能考虑沟通延迟对许多客户的非常大系统的行为的影响。在这项工作中，我们考虑了一个多代理负载平衡系统，其中包含延迟信息，包括许多客户（负载平衡器）和许多并行队列。为了获得可处理的解决方案，我们通过精确离散化在离散时间内将该系统建模为具有扩大状态行动空间的平均场控制问题。随后，我们应用政策梯度增强学习算法来找到最佳的负载平衡解决方案。在这里，离散时间系统模型包含了同步延迟，在该延迟下，在所有客户端，队列状态信息同步广播和更新。然后，我们在大型系统中为我们的方法提供了理论性能保证。最后，使用实验，我们证明了我们的方法不仅可扩展，而且与最新的Join-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the warriant相比，还表现出良好的性能（JSQ）和其他在同步延迟的情况下政策。

多功能钢筋学习方法在解决复杂的多助理问题中显示出显着的潜力，但大多数缺乏理论担保。最近，平均实地控制和平均野外游戏已被建立为具有许多代理的大规模多代理问题的贸易解决方案。在这项工作中，由激励调度问题驱动，我们考虑具有常见环境状态的离散时间均值实地控制模型。我们严格地建立了近似最优性，因为在有限的代理人案件中增长，发现动态编程原理保持，导致最佳静止政策的存在。由于由于限制平均场地马尔可夫决策过程所产生的连续动作空间，因此难以一般而言，我们应用建立的深度加强学习方法来解决相关的平均场控制问题。将学习的平均实地控制策略的性能与典型的多代理强化学习方法进行比较，并且被发现汇集到足够多种代理的平均场效，验证所获得的理论结果并达到竞争解决方案。

The aim of this study is to define importance of predictors for black box machine learning methods, where the prediction function can be highly non-additive and cannot be represented by statistical parameters. In this paper we defined a ``Generalized Variable Importance Metric (GVIM)'' using the true conditional expectation function for a continuous or a binary response variable. We further showed that the defined GVIM can be represented as a function of the Conditional Average Treatment Effect (CATE) squared for multinomial and continuous predictors. Then we propose how the metric can be estimated using using any machine learning models. Finally we showed the properties of the estimator using multiple simulations.

Detecting personal health mentions on social media is essential to complement existing health surveillance systems. However, annotating data for detecting health mentions at a large scale is a challenging task. This research employs a multitask learning framework to leverage available annotated data from a related task to improve the performance on the main task to detect personal health experiences mentioned in social media texts. Specifically, we focus on incorporating emotional information into our target task by using emotion detection as an auxiliary task. Our approach significantly improves a wide range of personal health mention detection tasks compared to a strong state-of-the-art baseline.

The health mention classification (HMC) task is the process of identifying and classifying mentions of health-related concepts in text. This can be useful for identifying and tracking the spread of diseases through social media posts. However, this is a non-trivial task. Here we build on recent studies suggesting that using emotional information may improve upon this task. Our study results in a framework for health mention classification that incorporates affective features. We present two methods, an intermediate task fine-tuning approach (implicit) and a multi-feature fusion approach (explicit) to incorporate emotions into our target task of HMC. We evaluated our approach on 5 HMC-related datasets from different social media platforms including three from Twitter, one from Reddit and another from a combination of social media sources. Extensive experiments demonstrate that our approach results in statistically significant performance gains on HMC tasks. By using the multi-feature fusion approach, we achieve at least a 3% improvement in F1 score over BERT baselines across all datasets. We also show that considering only negative emotions does not significantly affect performance on the HMC task. Additionally, our results indicate that HMC models infused with emotional knowledge are an effective alternative, especially when other HMC datasets are unavailable for domain-specific fine-tuning. The source code for our models is freely available at https://github.com/tahirlanre/Emotion_PHM.

Spatial perception is a key task in several robotics applications. In general, it involves the nonlinear estimation of hidden variables that represent the state of the robot/environment. However, in the presence of outliers the standard nonlinear least squared formulation results in poor estimates. Several methods have been considered in the literature to improve the reliability of the estimation process. Most methods are based on heuristics since guaranteed global robust estimation is not generally practical due to high computational costs. Recently general purpose robust estimation heuristics have been proposed that leverage existing non-minimal solvers available for the outlier-free formulations without the need for an initial guess. In this work, we propose two similar heuristics backed by Bayesian theory. We evaluate these heuristics in practical scenarios to demonstrate their merits in different applications including 3D point cloud registration, mesh registration and pose graph optimization.

Machine learning algorithms typically assume that the training and test samples come from the same distributions, i.e., in-distribution. However, in open-world scenarios, streaming big data can be Out-Of-Distribution (OOD), rendering these algorithms ineffective. Prior solutions to the OOD challenge seek to identify invariant features across different training domains. The underlying assumption is that these invariant features should also work reasonably well in the unlabeled target domain. By contrast, this work is interested in the domain-specific features that include both invariant features and features unique to the target domain. We propose a simple yet effective approach that relies on correlations in general regardless of whether the features are invariant or not. Our approach uses the most confidently predicted samples identified by an OOD base model (teacher model) to train a new model (student model) that effectively adapts to the target domain. Empirical evaluations on benchmark datasets show that the performance is improved over the SOTA by ~10-20%

Low-rank and sparse decomposition based methods find their use in many applications involving background modeling such as clutter suppression and object tracking. While Robust Principal Component Analysis (RPCA) has achieved great success in performing this task, it can take hundreds of iterations to converge and its performance decreases in the presence of different phenomena such as occlusion, jitter and fast motion. The recently proposed deep unfolded networks, on the other hand, have demonstrated better accuracy and improved convergence over both their iterative equivalents as well as over other neural network architectures. In this work, we propose a novel deep unfolded spatiotemporal RPCA (DUST-RPCA) network, which explicitly takes advantage of the spatial and temporal continuity in the low-rank component. Our experimental results on the moving MNIST dataset indicate that DUST-RPCA gives better accuracy when compared with the existing state of the art deep unfolded RPCA networks.