在这项工作中，我们提出了一个新的范式，用于使用降低性降低方案（包括Minibatch梯度近似和操作员草图）设计有效的深层展开网络。深度展开的网络目前是成像逆问题的最新解决方案。然而，对于高维成像任务，尤其是X射线CT和MRI成像，由于需要多次计算高维向前和邻接运算符，因此深层展开方案通常在记忆和计算方面效率低下。最近，研究人员发现，可以通过展开随机梯度下降（SGD）来部分解决此类局限性，这受到随机一阶优化的成功的启发。在这项工作中，我们基于最先进的原始偶（LPD）网络，进一步探讨了这一方向，并首先提出了更具表现力和实用的随机原始偶发性展开，也是随机原始的进一步加速 - 双曲线，使用素描技术在高维图像空间中近似产品。操作员素描可以与随机展开共同应用，以获得最佳的加速度和压缩性能。我们对X射线CT图像重建的数值实验证明了我们加速展开方案的显着有效性。

树种的手动标记仍然是一项具有挑战性的任务，尤其是在热带地区，由于无法获得和劳动密集型地面调查。高光谱图像（HSIS）通过其狭窄且连续的带，可以根据其光谱特性来区分树种。因此，HSI图像上的自动分类算法可以帮助增强有限的标记信息，并为各种树种生成实时分类图。通过图像中有限的标记信息，实现高分类精度是研究人员近年来开始应对的关键挑战之一。我们提出了一种新型的图形调节神经网络（GRNN）算法，该算法涵盖了基于超像素的分割用于图形结构，像素神经网络分类器和标签传播技术，以生成准确的分类图。 Grnn的表现不仅胜过标准的印度松树HSI的几种最先进技术，而且在法国圭亚那（FG）的森林中收集的新的HSI数据集上也达到了高分类的准确性（约92％）少于1％的像素被标记。我们表明，GRNN不仅与最先进的半监督方法具有竞争力，而且还表现出不同数量的训练样本的准确性差异和对标记像素进行培训的不同独立随机采样的差异。

近年来，深度学习在图像重建方面取得了显着的经验成功。这已经促进了对关键用例中数据驱动方法的正确性和可靠性的精确表征的持续追求，例如在医学成像中。尽管基于深度学习的方法具有出色的性能和功效，但对其稳定性或缺乏稳定性的关注以及严重的实际含义。近年来，已经取得了重大进展，以揭示数据驱动的图像恢复方法的内部运作，从而挑战了其广泛认为的黑盒本质。在本文中，我们将为数据驱动的图像重建指定相关的融合概念，该概念将构成具有数学上严格重建保证的学习方法调查的基础。强调的一个例子是ICNN的作用，提供了将深度学习的力量与经典凸正则化理论相结合的可能性，用于设计被证明是融合的方法。这篇调查文章旨在通过提供对数据驱动的图像重建方法以及从业人员的理解，旨在通过提供可访问的融合概念的描述，并通过将一些现有的经验实践放在可靠的数学上，来推进我们对数据驱动图像重建方法的理解以及从业人员的了解。基础。

在这项工作中，我们提出了一种新型高效的深度展开网络，用于解决成像逆问题。经典深度展开方法需要全向前运算符及其伴随各层，因此可以计算比其他端到端方法（如FBP-GROMNET）昂贵，尤其是在3D图像重建任务中。我们提出了一种具有所学习的原始双（LPD）的随机（订购子集）延伸，这是一种最先进的展开网络。在我们的展开网络中，我们只使用前向和伴随运营商的子集，以实现计算效率。我们为我们的LSPD框架内提供了对特殊情况的理论分析，这表明我们的LSPD网络有可能实现相同的完整批量LPD网络准确性，只能访问运营商的子集。我们的数值结果证明了我们在X射线CT成像任务中的方法的有效性，表明我们的网络实现了与全批次LPD相似的重建精度，同时只需要计算的一小部分。

We consider the problem of constructing minimax rate-optimal estimators for a doubly robust nonparametric functional that has witnessed applications across the causal inference and conditional independence testing literature. Minimax rate-optimal estimators for such functionals are typically constructed through higher-order bias corrections of plug-in and one-step type estimators and, in turn, depend on estimators of nuisance functions. In this paper, we consider a parallel question of interest regarding the optimality and/or sub-optimality of plug-in and one-step bias-corrected estimators for the specific doubly robust functional of interest. Specifically, we verify that by using undersmoothing and sample splitting techniques when constructing nuisance function estimators, one can achieve minimax rates of convergence in all H\"older smoothness classes of the nuisance functions (i.e. the propensity score and outcome regression) provided that the marginal density of the covariates is sufficiently regular. Additionally, by demonstrating suitable lower bounds on these classes of estimators, we demonstrate the necessity to undersmooth the nuisance function estimators to obtain minimax optimal rates of convergence.

Search and Rescue (SAR) missions in remote environments often employ autonomous multi-robot systems that learn, plan, and execute a combination of local single-robot control actions, group primitives, and global mission-oriented coordination and collaboration. Often, SAR coordination strategies are manually designed by human experts who can remotely control the multi-robot system and enable semi-autonomous operations. However, in remote environments where connectivity is limited and human intervention is often not possible, decentralized collaboration strategies are needed for fully-autonomous operations. Nevertheless, decentralized coordination may be ineffective in adversarial environments due to sensor noise, actuation faults, or manipulation of inter-agent communication data. In this paper, we propose an algorithmic approach based on adversarial multi-agent reinforcement learning (MARL) that allows robots to efficiently coordinate their strategies in the presence of adversarial inter-agent communications. In our setup, the objective of the multi-robot team is to discover targets strategically in an obstacle-strewn geographical area by minimizing the average time needed to find the targets. It is assumed that the robots have no prior knowledge of the target locations, and they can interact with only a subset of neighboring robots at any time. Based on the centralized training with decentralized execution (CTDE) paradigm in MARL, we utilize a hierarchical meta-learning framework to learn dynamic team-coordination modalities and discover emergent team behavior under complex cooperative-competitive scenarios. The effectiveness of our approach is demonstrated on a collection of prototype grid-world environments with different specifications of benign and adversarial agents, target locations, and agent rewards.

This paper presents a novel federated reinforcement learning (Fed-RL) methodology to enhance the cyber resiliency of networked microgrids. We formulate a resilient reinforcement learning (RL) training setup which (a) generates episodic trajectories injecting adversarial actions at primary control reference signals of the grid forming (GFM) inverters and (b) trains the RL agents (or controllers) to alleviate the impact of the injected adversaries. To circumvent data-sharing issues and concerns for proprietary privacy in multi-party-owned networked grids, we bring in the aspects of federated machine learning and propose a novel Fed-RL algorithm to train the RL agents. To this end, the conventional horizontal Fed-RL approaches using decoupled independent environments fail to capture the coupled dynamics in a networked microgrid, which leads us to propose a multi-agent vertically federated variation of actor-critic algorithms, namely federated soft actor-critic (FedSAC) algorithm. We created a customized simulation setup encapsulating microgrid dynamics in the GridLAB-D/HELICS co-simulation platform compatible with the OpenAI Gym interface for training RL agents. Finally, the proposed methodology is validated with numerical examples of modified IEEE 123-bus benchmark test systems consisting of three coupled microgrids.

Dataset Distillation (DD), a newly emerging field, aims at generating much smaller and high-quality synthetic datasets from large ones. Existing DD methods based on gradient matching achieve leading performance; however, they are extremely computationally intensive as they require continuously optimizing a dataset among thousands of randomly initialized models. In this paper, we assume that training the synthetic data with diverse models leads to better generalization performance. Thus we propose two \textbf{model augmentation} techniques, ~\ie using \textbf{early-stage models} and \textbf{weight perturbation} to learn an informative synthetic set with significantly reduced training cost. Extensive experiments demonstrate that our method achieves up to 20$\times$ speedup and comparable performance on par with state-of-the-art baseline methods.

Recent years have seen rapid progress at the intersection between causality and machine learning. Motivated by scientific applications involving high-dimensional data, in particular in biomedicine, we propose a deep neural architecture for learning causal relationships between variables from a combination of empirical data and prior causal knowledge. We combine convolutional and graph neural networks within a causal risk framework to provide a flexible and scalable approach. Empirical results include linear and nonlinear simulations (where the underlying causal structures are known and can be directly compared against), as well as a real biological example where the models are applied to high-dimensional molecular data and their output compared against entirely unseen validation experiments. These results demonstrate the feasibility of using deep learning approaches to learn causal networks in large-scale problems spanning thousands of variables.

The central question in representation learning is what constitutes a good or meaningful representation. In this work we argue that if we consider data with inherent cluster structures, where clusters can be characterized through different means and covariances, those data structures should be represented in the embedding as well. While Autoencoders (AE) are widely used in practice for unsupervised representation learning, they do not fulfil the above condition on the embedding as they obtain a single representation of the data. To overcome this we propose a meta-algorithm that can be used to extend an arbitrary AE architecture to a tensorized version (TAE) that allows for learning cluster-specific embeddings while simultaneously learning the cluster assignment. For the linear setting we prove that TAE can recover the principle components of the different clusters in contrast to principle component of the entire data recovered by a standard AE. We validated this on planted models and for general, non-linear and convolutional AEs we empirically illustrate that tensorizing the AE is beneficial in clustering and de-noising tasks.