Classical differential private DP-SGD implements individual clipping with random subsampling, which forces a mini-batch SGD approach. We provide a general differential private algorithmic framework that goes beyond DP-SGD and allows any possible first order optimizers (e.g., classical SGD and momentum based SGD approaches) in combination with batch clipping, which clips an aggregate of computed gradients rather than summing clipped gradients (as is done in individual clipping). The framework also admits sampling techniques beyond random subsampling such as shuffling. Our DP analysis follows the $f$-DP approach and introduces a new proof technique which allows us to also analyse group privacy. In particular, for $E$ epochs work and groups of size $g$, we show a $\sqrt{g E}$ DP dependency for batch clipping with shuffling. This is much better than the previously anticipated linear dependency in $g$ and is much better than the previously expected square root dependency on the total number of rounds within $E$ epochs which is generally much more than $\sqrt{E}$.

排队系统出现在许多重要的现实生活应用中，包括通信网络，运输和制造系统。加固学习（RL）框架是排队控制问题的合适模型，在该问题中，基础动力通常未知，并且代理几乎没有从环境中接收到导航的信息。在这项工作中，我们将排队模型作为RL环境的优化方面进行了研究，并提供了有效学习最佳政策的见解。我们通过使用排队网络系统的固有属性来提出策略的新参数化。实验显示了我们的方法的良好性能，从轻度到繁忙的交通状况各种负载条件。

随机梯度下降（SGD）算法是许多机器学习任务中选择的方法，这要归功于其在处理大规模问题方面的可扩展性和效率。在本文中，我们专注于与主流实践启发式符合SGD的改组版。我们将收敛性与过度参数化设置下的一类非凸功率函数的全局解决方案展示为全局解决方案。与以前的文献相比，我们的分析采用更轻松的非凸假设。然而，我们保持了所需的计算复杂性，因为改组SGD在一般凸设置中已实现。

我们研究了两种可能不同质量的度量之间的不平衡最佳运输（UOT），其中最多是$ n $组件，其中标准最佳运输（OT）的边际约束是通过kullback-leibler差异与正则化因子$ \ tau $放松的。尽管仅在文献中分析了具有复杂性$ o \ big（\ tfrac {\ tau n^2 \ log（n）} {\ varepsilon} \ log \ big（\ tfrac {\ log（ n）} {{{\ varepsilon}} \ big）\ big）$）$用于实现错误$ \ varepsilon $，它们与某些深度学习模型和密集的输出运输计划不兼容，强烈阻碍了实用性。虽然被广泛用作计算现代深度学习应用中UOT的启发式方法，并且在稀疏的OT中表现出成功，但尚未正式研究用于UOT的梯度方法。为了填补这一空白，我们提出了一种基于梯度外推法（Gem-uot）的新颖算法，以找到$ \ varepsilon $ -Approximate解决方案，以解决$ o \ big中的UOT问题（\ kappa n^2 \ log \ log \ big（big） \ frac {\ tau n} {\ varepsilon} \ big）\ big）$，其中$ \ kappa $是条件号，具体取决于两个输入度量。我们的算法是通过优化平方$ \ ell_2 $ -norm UOT目标的新的双重配方设计的，从而填补了缺乏稀疏的UOT文献。最后，我们在运输计划和运输距离方面建立了UOT和OT之间近似误差的新颖表征。该结果阐明了一个新的主要瓶颈，该瓶颈被强大的OT文献忽略了：尽管OT放松了OT，因为UOT承认对离群值的稳健性，但计算出的UOT距离远离原始OT距离。我们通过基于Gem-uot从UOT中检索的原则方法来解决此类限制，并使用微调的$ \ tau $和后进程投影步骤来解决。关于合成和真实数据集的实验验证了我们的理论，并证明了我们的方法的良好性能。

在本文中，我们提出了Nesterov加速改组梯度（NASG），这是一种用于凸有限和最小化问题的新算法。我们的方法将传统的Nesterov的加速动量与不同的改组抽样方案相结合。我们证明，我们的算法使用统一的改组方案提高了$ \ Mathcal {o}（1/t）$的速率，其中$ t $是时代的数量。该速率比凸状制度中的任何其他改组梯度方法要好。我们的收敛分析不需要对有限域或有界梯度条件的假设。对于随机洗牌方案，我们进一步改善了收敛性。在采用某种初始条件时，我们表明我们的方法在解决方案的小社区附近收敛得更快。数值模拟证明了我们算法的效率。

聚类是一个流行的无监督学习工具，通常用于发现较大的人口中的群体，例如客户段或患者亚型。但是，尽管它用作子组发现的工具和描述 - 很少有最先进的算法提供了发现的群集后面的任何理由或描述。我们提出了一种用于可解释聚类的新方法，即群集数据点和构建在被发现的集群周围的多个群体来解释它们。我们的框架允许在多台上进行额外的约束 - 包括确保构建多托的超平面是轴平行的或稀疏，具有整数系数。我们制定通过多拓构造群集作为混合整数非线性程序（MINLP）的问题。要解决我们的配方，我们提出了一种两相方法，我们首先使用交替的最小化初始化群集和多核酸，然后使用坐标下降来提升聚类性能。我们在一套综合和真实的世界聚类问题上基准测试方法，其中我们的算法优于艺术可解释和不可解释的聚类算法的状态。

最近的研究表明，通过梯度下降训练的无限宽神经网络（NN）的动态可以是神经切线核（NTK）\ CITEP {Jacot2018neural}的特征。在平方损失下，通过梯度下降训练的无限宽度NN，具有无限小的学习速率等同于与NTK \ CITEP {arora2019Exact}的内核回归。但是，当前ridge回归{arora2019Harnessing}只知道等价物，而NN和其他内核机（KMS）之间的等价，例如，支持向量机（SVM），仍然未知。因此，在这项工作中，我们建议在NN和SVM之间建立等效，具体而言，通过柔软的边缘损失和具有由子润发性培训的NTK培训的标准柔软裕度SVM培训的无限宽NN。我们的主要理论结果包括建立NN和广泛的$ \ ELL_2 $正规化KMS之间的等价，其中有限宽度界限，不能通过事先工作来处理，并显示出通过这种正规化损耗函数训练的每个有限宽度NN大约一公里。此外，我们展示了我们的理论可以实现三种实际应用，包括（i）\ yressit {非空心}通过相应的km界限Nn; （ii）无限宽度NN的\ yryit {非琐碎}鲁棒性证书（而现有的鲁棒性验证方法提供空中界定）; （iii）本质上更强大的无限宽度NN，来自以前的内核回归。我们的实验代码可用于\ URL {https://github.com/leslie-ch/equiv-nn-svm}。

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. Our DRG-Net consists of two modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas, and provide visual explanations; (ii) DRG-Expert-Interaction to receive feedback from user-expert and improve the DRG-AI-System. To deal with sparse data, we utilize transfer learning mechanisms to extract invariant feature representations by using Wasserstein distance and adversarial learning-based entropy minimization. Besides, we propose a novel attention strategy at both low- and high-level features to automatically select the most significant lesion information and provide explainable properties. In terms of human interaction, we further develop DRG-Net as a tool that enables expert users to correct the system's predictions, which may then be used to update the system as a whole. Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback of users. We have benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and compared it to various state-of-the-art deep learning networks. In addition to outperforming other SOTA approaches, DRG-Net is effectively updated using user feedback, even in a weakly-supervised manner.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Manually analyzing spermatozoa is a tremendous task for biologists due to the many fast-moving spermatozoa, causing inconsistencies in the quality of the assessments. Therefore, computer-assisted sperm analysis (CASA) has become a popular solution. Despite this, more data is needed to train supervised machine learning approaches in order to improve accuracy and reliability. In this regard, we provide a dataset called VISEM-Tracking with 20 video recordings of 30s of spermatozoa with manually annotated bounding-box coordinates and a set of sperm characteristics analyzed by experts in the domain. VISEM-Tracking is an extension of the previously published VISEM dataset. In addition to the annotated data, we provide unlabeled video clips for easy-to-use access and analysis of the data. As part of this paper, we present baseline sperm detection performances using the YOLOv5 deep learning model trained on the VISEM-Tracking dataset. As a result, the dataset can be used to train complex deep-learning models to analyze spermatozoa. The dataset is publicly available at https://zenodo.org/record/7293726.