We consider the decentralized exploration problem: a set of players collaborate to identify the best arm by asynchronously interacting with the same stochastic environment. The objective is to insure privacy in the best arm identification problem between asynchronous, collaborative, and thrifty players. In the context of a digital service, we advocate that this decentralized approach allows a good balance between the interests of users and those of service providers: the providers optimize their services, while protecting the privacy of the users and saving resources. We define the privacy level as the amount of information an adversary could infer by intercepting the messages concerning a single user. We provide a generic algorithm Decentralized Elimination, which uses any best arm identification algorithm as a subroutine. We prove that this algorithm insures privacy, with a low communication cost, and that in comparison to the lower bound of the best arm identification problem, its sample complexity suffers from a penalty depending on the inverse of the probability of the most frequent players. Then, thanks to the genericity of the approach, we extend the proposed algorithm to the non-stationary bandits. Finally, experiments illustrate and complete the analysis.

Physics-Informed Neural Networks (PINNs) have gained much attention in various fields of engineering thanks to their capability of incorporating physical laws into the models. PINNs integrate the physical constraints by minimizing the partial differential equations (PDEs) residuals on a set of collocation points. The distribution of these collocation points appears to have a huge impact on the performance of PINNs and the assessment of the sampling methods for these points is still an active topic. In this paper, we propose a Fixed-Budget Online Adaptive Mesh Learning (FBOAML) method, which decomposes the domain into sub-domains, for training collocation points based on local maxima and local minima of the PDEs residuals. The stopping criterion is based on a data set of reference, which leads to an adaptive number of iterations for each specific problem. The effectiveness of FBOAML is demonstrated in the context of non-parameterized and parameterized problems. The impact of the hyper-parameters in FBOAML is investigated in this work. The comparison with other adaptive sampling methods is also illustrated. The numerical results demonstrate important gains in terms of accuracy of PINNs with FBOAML over the classical PINNs with non-adaptive collocation points. We also apply FBOAML in a complex industrial application involving coupling between mechanical and thermal fields. We show that FBOAML is able to identify the high-gradient location and even give better prediction for some physical fields than the classical PINNs with collocation points taken on a pre-adapted finite element mesh.

Common measures of brain functional connectivity (FC) including covariance and correlation matrices are semi-positive definite (SPD) matrices residing on a cone-shape Riemannian manifold. Despite its remarkable success for Euclidean-valued data generation, use of standard generative adversarial networks (GANs) to generate manifold-valued FC data neglects its inherent SPD structure and hence the inter-relatedness of edges in real FC. We propose a novel graph-regularized manifold-aware conditional Wasserstein GAN (GR-SPD-GAN) for FC data generation on the SPD manifold that can preserve the global FC structure. Specifically, we optimize a generalized Wasserstein distance between the real and generated SPD data under an adversarial training, conditioned on the class labels. The resulting generator can synthesize new SPD-valued FC matrices associated with different classes of brain networks, e.g., brain disorder or healthy control. Furthermore, we introduce additional population graph-based regularization terms on both the SPD manifold and its tangent space to encourage the generator to respect the inter-subject similarity of FC patterns in the real data. This also helps in avoiding mode collapse and produces more stable GAN training. Evaluated on resting-state functional magnetic resonance imaging (fMRI) data of major depressive disorder (MDD), qualitative and quantitative results show that the proposed GR-SPD-GAN clearly outperforms several state-of-the-art GANs in generating more realistic fMRI-based FC samples. When applied to FC data augmentation for MDD identification, classification models trained on augmented data generated by our approach achieved the largest margin of improvement in classification accuracy among the competing GANs over baselines without data augmentation.

We aim at improving reasoning on inconsistent and uncertain data. We focus on knowledge-graph data, extended with time intervals to specify their validity, as regularly found in historical sciences. We propose principles on semantics for efficient Maximum A-Posteriori inference on the new Temporal Markov Logic Networks (TMLN) which extend the Markov Logic Networks (MLN) by uncertain temporal facts and rules. We examine total and partial temporal (in)consistency relations between sets of temporal formulae. Then we propose a new Temporal Parametric Semantics, which may combine several sub-functions, allowing to use different assessment strategies. Finally, we expose the constraints that semantics must respect to satisfy our principles.

在过去几十年中，功能选择吸引了很多关注，因为它可以降低数据维度，同时保持功能的原始物理含义，这比功能提取可以更好地解释性。但是，大多数现有的功能选择方法，尤其是基于深度学习的方法，通常集中在仅具有很高分数的功能上，但忽略了那些在训练过程中得分较低的人以及重要的候选功能的顺序。这可能是有风险的，因为不幸的是，在培训过程中可能会忽略一些重要和相关的功能，从而导致次优的解决方案或误导性选择。在我们的工作中，我们通过利用较少重要性分数的功能来处理功能选择，并根据新颖的互补功能掩码提出功能选择框架。我们的方法是通用的，可以轻松地集成到现有的基于深度学习的特征选择方法中，以提高其性能。实验是在基准数据集上进行的，并表明所提出的方法可以选择比艺术状态更具代表性和信息性的特征。

光伏（PV）能量产生在能量转变中起着至关重要的作用。小规模的PV安装以空前的速度部署，并且它们在电网中的集成可能会具有挑战性，因为公共当局通常缺乏有关它们的质量数据。越来越多的机器学习模型能够自动映射这些安装，越来越多地用于改善住宅PV安装的知识。但是，由于图像采集的差异，这些模型不能轻易地从一个区域或数据源转移到另一个区域。为了解决此问题，称为域移动并促进了PV阵列映射管道的开发，我们提出了一个包含空中图像，注释和分割掩码的数据集。我们为28,000多个安装提供安装元数据。我们为13,000个装置提供地面真理细分面具，其中包括7,000个带有两个不同图像提供商的注释。最后，我们提供了与8,000多个安装的注释相匹配的安装元数据。数据集应用程序包括端到端的PV注册表构建，强大的PV安装映射以及众包数据集的分析。

我们介绍Audiolm，这是具有长期一致性高质量音频产生的框架。 Audiolm将输入音频映射到一系列离散令牌，并将音频生成作为此表示空间中的语言建模任务。我们展示了现有的音频令牌如何在重建质量和长期结构之间提供不同的权衡，我们提出了一个混合代币化计划来实现这两个目标。也就是说，我们利用在音频中预先训练的蒙版语言模型的离散激活来捕获长期结构和神经音频编解码器产生的离散代码，以实现高质量的合成。通过培训大型原始音频波形，Audiolm学会了在简短的提示下产生自然和连贯的连续性。当接受演讲训练时，没有任何笔录或注释，Audiolm会在句法和语义上产生可行的语音连续性，同时还为看不见的说话者保持说话者身份和韵律。此外，我们演示了我们的方法如何通过产生连贯的钢琴音乐连续性来超越语音，尽管受过训练而没有任何象征性的音乐代表。

对角线性网络（DLN）是人工神经网络的玩具。它们由线性回归的二次重复化，诱导稀疏的隐式正则化。在本文中，我们描述了DLNS梯度流的轨迹，在小初始化的极限中。我们表明，增量学习有效地在限制中进行：坐标是连续激活的，而迭代是最小化的损耗的最小化器，仅在有源坐标上得到支持。这表明DLN的稀疏隐式正则化随时间而减小。由于技术原因，这项工作仅限于具有反相关特征的隔离化制度。

神经网络最近显示出对无似然推理的希望，从而为经典方法提供了魔力的速度。但是，当从独立重复估计参数时，当前的实现是次优的。在本文中，我们使用决策理论框架来争辩说，如果这些模型的模拟很简单，则理想地放置了置换不变的神经网络，可用于为任意模型构造贝叶斯估计器。我们说明了这些估计量在传统空间模型以及高度参数化的空间发射模型上的潜力，并表明它们在其网络设计中不适当地说明复制的神经估计量相当大。同时，它们比基于传统可能性的估计量具有很高的竞争力和更快的速度。我们将估计量应用于红海中海面温度的空间分析，在训练之后，我们获得参数估计值，并通过引导采样对估计值进行不确定性定量，从一秒钟的数百个空间场中获取。

元学习是一项研究线，它可以发展出利用过去的经验来有效解决新的学习问题的能力。元强化学习（META-RL）方法证明了学习行为的能力，这些行为有效地获取和利用了几个Meta-RL问题中的信息。在这种情况下，Wang等人提出了炼金术基准。 [2021]。炼金术具有丰富的结构性潜在空间，对最新的无模型RL方法具有挑战性。这些方法无法学会正确探索然后利用。我们开发了一种基于模型的算法。我们训练一个模型，其主要块是适合符号炼金术环境动力学的变压器编码器。然后，我们使用树搜索方法定义了通过学习模型的在线计划者。该算法在符号炼金术问题上显着优于先前应用的无模型RL方法。我们的结果揭示了基于模型的方法与在线计划在Meta-RL中成功执行探索和剥削的相关性。此外，我们展示了变压器体系结构的效率，以学习来自元RL问题中存在的潜在空间产生的复杂动力学。