这项研究重点是探索局部可解释性方法来解释时间序列聚类模型。许多最先进的聚类模型无法直接解释。为了提供这些聚类算法的解释，我们训练分类模型以估计群集标签。然后，我们使用可解释性方法来解释分类模型的决策。这些解释用于获得对聚类模型的见解。我们执行一项详细的数值研究，以测试多个数据集，聚类模型和分类模型上所提出的方法。结果的分析表明，所提出的方法可用于解释时间序列聚类模型，特别是当基础分类模型准确时。最后，我们对结果进行了详细的分析，讨论了如何在现实生活中使用我们的方法。

Privacy protection and nonconvexity are two challenging problems in decentralized optimization and learning involving sensitive data. Despite some recent advances addressing each of the two problems separately, no results have been reported that have theoretical guarantees on both privacy protection and saddle/maximum avoidance in decentralized nonconvex optimization. We propose a new algorithm for decentralized nonconvex optimization that can enable both rigorous differential privacy and saddle/maximum avoiding performance. The new algorithm allows the incorporation of persistent additive noise to enable rigorous differential privacy for data samples, gradients, and intermediate optimization variables without losing provable convergence, and thus circumventing the dilemma of trading accuracy for privacy in differential privacy design. More interestingly, the algorithm is theoretically proven to be able to efficiently { guarantee accuracy by avoiding} convergence to local maxima and saddle points, which has not been reported before in the literature on decentralized nonconvex optimization. The algorithm is efficient in both communication (it only shares one variable in each iteration) and computation (it is encryption-free), and hence is promising for large-scale nonconvex optimization and learning involving high-dimensional optimization parameters. Numerical experiments for both a decentralized estimation problem and an Independent Component Analysis (ICA) problem confirm the effectiveness of the proposed approach.

This study considers a federated learning setup where cost-sensitive and strategic agents train a learning model with a server. During each round, each agent samples a minibatch of training data and sends his gradient update. As an increasing function of his minibatch size choice, the agent incurs a cost associated with the data collection, gradient computation and communication. The agents have the freedom to choose their minibatch size and may even opt out from training. To reduce his cost, an agent may diminish his minibatch size, which may also cause an increase in the noise level of the gradient update. The server can offer rewards to compensate the agents for their costs and to incentivize their participation but she lacks the capability of validating the true minibatch sizes of the agents. To tackle this challenge, the proposed reward mechanism evaluates the quality of each agent's gradient according to the its distance to a reference which is constructed from the gradients provided by other agents. It is shown that the proposed reward mechanism has a cooperative Nash equilibrium in which the agents determine the minibatch size choices according to the requests of the server.

As the interest to Graph Neural Networks (GNNs) is growing, the importance of benchmarking and performance characterization studies of GNNs is increasing. So far, we have seen many studies that investigate and present the performance and computational efficiency of GNNs. However, the work done so far has been carried out using a few high-level GNN frameworks. Although these frameworks provide ease of use, they contain too many dependencies to other existing libraries. The layers of implementation details and the dependencies complicate the performance analysis of GNN models that are built on top of these frameworks, especially while using architectural simulators. Furthermore, different approaches on GNN computation are generally overlooked in prior characterization studies, and merely one of the common computational models is evaluated. Based on these shortcomings and needs that we observed, we developed a benchmark suite that is framework independent, supporting versatile computational models, easily configurable and can be used with architectural simulators without additional effort. Our benchmark suite, which we call gSuite, makes use of only hardware vendor's libraries and therefore it is independent of any other frameworks. gSuite enables performing detailed performance characterization studies on GNN Inference using both contemporary GPU profilers and architectural GPU simulators. To illustrate the benefits of our new benchmark suite, we perform a detailed characterization study with a set of well-known GNN models with various datasets; running gSuite both on a real GPU card and a timing-detailed GPU simulator. We also implicate the effect of computational models on performance. We use several evaluation metrics to rigorously measure the performance of GNN computation.

通过使多个代理在缺乏中央协调员的情况下合作解决全球优化问题，分散的随机优化在像机器学习，控制和传感器网络这样的多种多样的领域中，人们的注意力越来越多。由于相关数据通常包含敏感信息，例如用户位置和个人身份，因此在实施分散的随机优化时，隐私保护已成为至关重要的需求。在本文中，我们提出了一种分散的随机优化算法，即使在存在与量化幅度成正比的积极量化误差的情况下，该算法也能够保证可证明的收敛精度。该结果同时适用于凸面和非凸目标函数，使我们能够利用积极的量化方案来混淆共享信息，因此可以在不失去可证明的优化精度的情况下进行隐私保护。实际上，通过使用将任何值量化为三个数值级别的任何值的{随机}三元量化方案，我们在分散的随机优化中实现了基于量化的严格差异隐私，以前尚未报告。结合提出的量化方案，提出的算法首次确保了分散的随机优化中的严格差异隐私，而不会失去可证明的收敛精度。分布式估计问题以及基准计算机学习数据集上分散学习的数值实验的仿真结果证实了所提出方法的有效性。

我们考虑一个多代理网络，其中每个节点具有随机（本地）成本函数，这取决于该节点的决策变量和随机变量，并且进一步的相邻节点的判定变量是成对受约束的。网络具有总体目标函数，其在节点处的本地成本函数的预期值ack，以及网络的总体目标是将该聚合目标函数的最小化解决方案最小化为所有成对约束。这将在节点级别使用分散的信息和本地计算来实现，其中仅具有相邻节点允许的压缩信息的交换。该文件开发算法，并在节点上获得两个不同型号的本地信息可用性模型的性能界限：（i）样本反馈，其中每个节点可以直接访问局部随机变量的样本，以评估其本地成本，（ii）babrit反馈，其中无随机变量的样本不可用，但只有每个节点可用的两个随机点处的本地成本函数的值可用。对于两种模型，具有邻居之间的压缩通信，我们开发了分散的骑马点算法，从没有通信压缩的那些没有不同（符号意义）的表现;具体而言，我们表明，与全局最小值和违反约束的偏差是由$ \ mathcal {o}的大约限制（t ^ { - \ frac {1} {2}}）$和$ \ mathcal {o} （t ^ { - \ frac {1} {4}}）分别为$ t $是迭代次数。本文中提供的数值例子证实了这些界限并证明了所提出的方法的通信效率。

我们在无限地平线上享受多智能经纪增强学习（Marl）零汇率马尔可夫游戏。我们专注于分散的Marl的实用性但具有挑战性的环境，其中代理人在没有集中式控制员的情况下做出决定，但仅根据自己的收益和当地行动进行了协调。代理商不需要观察对手的行为或收益，可能甚至不忘记对手的存在，也不得意识到基础游戏的零金额结构，该环境也称为学习文学中的彻底解散游戏。在本文中，我们开发了一种彻底的解耦Q学习动态，既合理和收敛则：当对手遵循渐近静止战略时，学习动态会收敛于对对手战略的最佳反应;当两个代理采用学习动态时，它们会收敛到游戏的纳什均衡。这种分散的环境中的关键挑战是从代理商的角度来看环境的非公平性，因为她自己的回报和系统演变都取决于其他代理人的行为，每个代理商同时和独立地互补她的政策。要解决此问题，我们开发了两个时间尺度的学习动态，每个代理会更新她的本地Q函数和value函数估计，后者在较慢的时间内发生。