当数据自然分配到通过基础图的代理商之间，分散学习提供了隐私和沟通效率。通过过度参数化的学习设置，在该设置中，在该设置中训练了零训练损失，我们研究了分散学习的分散学习算法和概括性能，并在可分离的数据上下降。具体而言，对于分散的梯度下降（DGD）和各种损失函数，在无穷大（包括指数损失和逻辑损失）中渐近为零，我们得出了新的有限时间泛化界限。这补充了一长串最近的工作，该工作研究了概括性能和梯度下降的隐含偏见，而不是可分离的数据，但迄今为止，梯度下降的偏见仅限于集中学习方案。值得注意的是，我们的概括范围匹配其集中式同行。这背后的关键和独立感兴趣的是，在一类自我结合的损失方面建立了关于训练损失和DGD的传记率的新界限。最后，在算法方面，我们设计了改进的基于梯度的例程，可分离数据，并在经验上证明了训练和概括性能方面的加速命令。

神经塌陷是指表征类嵌入和分类器重量的几何形状的显着结构特性，当经过零训练误差以外的训练时，深网被发现。但是，这种表征仅适用于平衡数据。因此，我们在这里询问是否可以使阶级失衡不变。为此，我们采用了不受限制的功能模型（UFM），这是一种用于研究神经塌陷的最新理论模型，并引入了单纯形编码标签的插值（SELI）作为神经崩溃现象的不变特征。具体而言，我们证明了UFM的跨凝结损失和消失的正则化，无论阶级失衡如何，嵌入和分类器总是插入单纯形编码的标签矩阵，并且其单个几何形状都由同一标签矩阵矩阵矩阵的SVD因子确定。然后，我们对合成和真实数据集进行了广泛的实验，这些实验确认了与SELI几何形状的收敛。但是，我们警告说，融合会随着不平衡的增加而恶化。从理论上讲，我们通过表明与平衡的情况不同，当存在少数民族时，山脊规范化在调整几何形状中起着至关重要的作用。这定义了新的问题，并激发了对阶级失衡对一阶方法融合其渐近优先解决方案的速率的影响的进一步研究。

过度参数化模型即使与传统的减轻失衡技术结合使用，在存在数据失衡的情况下也无法很好地概括。本文着重于分类数据集，其中一小部分人口（少数​​）可能包含与类标签相关的功能。对于跨凝结损失修饰和代表性高斯混合模型的参数家族，我们在最严重的组误差上得出了非反应泛化的边界，该误差揭示了不同的超参数的作用。具体而言，我们证明，在适当调整后，最近提出的VS-Loss学会了一个模型，即使伪造的特征很强，也对少数群体也是公平的。另一方面，替代性启发式方法，例如加权CE和LA-loss，可能会急剧失败。与以前的作品相比，我们的界限适用于更多的通用模型，它们是非吸血管的，即使在极端不平衡的情况下，它们也适用。

标准联合优化方法成功地适用于单层结构的随机问题。然而，许多当代的ML问题 - 包括对抗性鲁棒性，超参数调整和参与者 - 批判性 - 属于嵌套的双层编程，这些编程包含微型型和组成优化。在这项工作中，我们提出了\ fedblo：一种联合交替的随机梯度方法来解决一般的嵌套问题。我们在存在异质数据的情况下为\ fedblo建立了可证明的收敛速率，并引入了二聚体，最小值和组成优化的变化。\ fedblo引入了多种创新，包括联邦高级计算和降低方差，以解决内部级别的异质性。我们通过有关超参数\＆超代理学习和最小值优化的实验来补充我们的理论，以证明我们方法在实践中的好处。代码可在https://github.com/ucr-optml/fednest上找到。

现代机器学习问题中的不平衡数据集是司空见惯的。具有敏感属性的代表性课程或群体的存在导致关于泛化和公平性的担忧。这种担忧进一步加剧了大容量深网络可以完全适合培训数据，似乎在训练期间达到完美的准确性和公平，但在测试期间表现不佳。为了解决这些挑战，我们提出了自动化，一个自动设计培训损失功能的双层优化框架，以优化准确性和寻求公平目标的混合。具体地，较低级别的问题列举了模型权重，并且上级问题通过监视和优化通过验证数据的期望目标来调谐损耗功能。我们的损耗设计通过采用参数跨熵损失和个性化数据增强方案，可以为类/组进行个性化处理。我们评估我们对不平衡和群体敏感分类的应用方案的方法的好处和性能。广泛的经验评估表明了自动矛盾最先进的方法的益处。我们的实验结果与损耗功能设计的理论见解和培训验证分裂的好处相辅相成。所有代码都是可用的开源。

标签 - 不平衡和组敏感分类中的目标是优化相关的指标，例如平衡错误和相同的机会。经典方法，例如加权交叉熵，在训练深网络到训练（TPT）的终端阶段时，这是超越零训练误差的训练。这种观察发生了最近在促进少数群体更大边值的直观机制之后开发启发式替代品的动力。与之前的启发式相比，我们遵循原则性分析，说明不同的损失调整如何影响边距。首先，我们证明，对于在TPT中训练的所有线性分类器，有必要引入乘法，而不是添加性的Logit调整，以便对杂项边缘进行适当的变化。为了表明这一点，我们发现将乘法CE修改的连接到成本敏感的支持向量机。也许是违反，我们还发现，在培训开始时，相同的乘法权重实际上可以损害少数群体。因此，虽然在TPT中，添加剂调整无效，但我们表明它们可以通过对乘法重量的初始负效应进行抗衡来加速会聚。通过这些发现的动机，我们制定了矢量缩放（VS）丢失，即捕获现有技术作为特殊情况。此外，我们引入了对群体敏感分类的VS损失的自然延伸，从而以统一的方式处理两种常见类型的不平衡（标签/组）。重要的是，我们对最先进的数据集的实验与我们的理论见解完全一致，并确认了我们算法的卓越性能。最后，对于不平衡的高斯 - 混合数据，我们执行泛化分析，揭示平衡/标准错误和相同机会之间的权衡。

Given a large graph with few node labels, how can we (a) identify the mixed network-effect of the graph and (b) predict the unknown labels accurately and efficiently? This work proposes Network Effect Analysis (NEA) and UltraProp, which are based on two insights: (a) the network-effect (NE) insight: a graph can exhibit not only one of homophily and heterophily, but also both or none in a label-wise manner, and (b) the neighbor-differentiation (ND) insight: neighbors have different degrees of influence on the target node based on the strength of connections. NEA provides a statistical test to check whether a graph exhibits network-effect or not, and surprisingly discovers the absence of NE in many real-world graphs known to have heterophily. UltraProp solves the node classification problem with notable advantages: (a) Accurate, thanks to the network-effect (NE) and neighbor-differentiation (ND) insights; (b) Explainable, precisely estimating the compatibility matrix; (c) Scalable, being linear with the input size and handling graphs with millions of nodes; and (d) Principled, with closed-form formula and theoretical guarantee. Applied on eight real-world graph datasets, UltraProp outperforms top competitors in terms of accuracy and run time, requiring only stock CPU servers. On a large real-world graph with 1.6M nodes and 22.3M edges, UltraProp achieves more than 9 times speedup (12 minutes vs. 2 hours) compared to most competitors.

High content imaging assays can capture rich phenotypic response data for large sets of compound treatments, aiding in the characterization and discovery of novel drugs. However, extracting representative features from high content images that can capture subtle nuances in phenotypes remains challenging. The lack of high-quality labels makes it difficult to achieve satisfactory results with supervised deep learning. Self-Supervised learning methods, which learn from automatically generated labels has shown great success on natural images, offer an attractive alternative also to microscopy images. However, we find that self-supervised learning techniques underperform on high content imaging assays. One challenge is the undesirable domain shifts present in the data known as batch effects, which may be caused by biological noise or uncontrolled experimental conditions. To this end, we introduce Cross-Domain Consistency Learning (CDCL), a novel approach that is able to learn in the presence of batch effects. CDCL enforces the learning of biological similarities while disregarding undesirable batch-specific signals, which leads to more useful and versatile representations. These features are organised according to their morphological changes and are more useful for downstream tasks - such as distinguishing treatments and mode of action.

While risk-neutral reinforcement learning has shown experimental success in a number of applications, it is well-known to be non-robust with respect to noise and perturbations in the parameters of the system. For this reason, risk-sensitive reinforcement learning algorithms have been studied to introduce robustness and sample efficiency, and lead to better real-life performance. In this work, we introduce new model-free risk-sensitive reinforcement learning algorithms as variations of widely-used Policy Gradient algorithms with similar implementation properties. In particular, we study the effect of exponential criteria on the risk-sensitivity of the policy of a reinforcement learning agent, and develop variants of the Monte Carlo Policy Gradient algorithm and the online (temporal-difference) Actor-Critic algorithm. Analytical results showcase that the use of exponential criteria generalize commonly used ad-hoc regularization approaches. The implementation, performance, and robustness properties of the proposed methods are evaluated in simulated experiments.

Hierarchical learning algorithms that gradually approximate a solution to a data-driven optimization problem are essential to decision-making systems, especially under limitations on time and computational resources. In this study, we introduce a general-purpose hierarchical learning architecture that is based on the progressive partitioning of a possibly multi-resolution data space. The optimal partition is gradually approximated by solving a sequence of optimization sub-problems that yield a sequence of partitions with increasing number of subsets. We show that the solution of each optimization problem can be estimated online using gradient-free stochastic approximation updates. As a consequence, a function approximation problem can be defined within each subset of the partition and solved using the theory of two-timescale stochastic approximation algorithms. This simulates an annealing process and defines a robust and interpretable heuristic method to gradually increase the complexity of the learning architecture in a task-agnostic manner, giving emphasis to regions of the data space that are considered more important according to a predefined criterion. Finally, by imposing a tree structure in the progression of the partitions, we provide a means to incorporate potential multi-resolution structure of the data space into this approach, significantly reducing its complexity, while introducing hierarchical feature extraction properties similar to certain classes of deep learning architectures. Asymptotic convergence analysis and experimental results are provided for clustering, classification, and regression problems.