许多组织使用配备有加速器的Compute集群，例如GPU和TPU，用于以分布式方式培训深入学习模型。培训是资源密集型的，消耗显着的计算，内存和网络资源。许多先前的作品探索如何减少培训资源占资源的占资源占用空间，而不会影响质量，但它们对瓶颈的子集（通常只有网络）限制了它们改善整体集群利用的能力。在这项工作中，我们利用深度学习工作负载的独特特征来提出结构化部分反向化（SPB），这是一种系统地控制分布式培训中个别工人的背包量的技术。这同时可以减少网络带宽，计算利用率和内存占用空间，同时保持模型质量。为了有效地利用SPB在集群层面的好处，我们介绍了一个SPB了解调度程序的jigsaw，它在深度学习培训（DLT）作业中进行迭代级别。我们发现拼图可以通过高达28 \％将大规模集群效率提高。

We consider a long-term average profit maximizing admission control problem in an M/M/1 queuing system with a known arrival rate but an unknown service rate. With a fixed reward collected upon service completion and a cost per unit of time enforced on customers waiting in the queue, a dispatcher decides upon arrivals whether to admit the arriving customer or not based on the full history of observations of the queue-length of the system. \cite[Econometrica]{Naor} showed that if all the parameters of the model are known, then it is optimal to use a static threshold policy - admit if the queue-length is less than a predetermined threshold and otherwise not. We propose a learning-based dispatching algorithm and characterize its regret with respect to optimal dispatch policies for the full information model of \cite{Naor}. We show that the algorithm achieves an $O(1)$ regret when all optimal thresholds with full information are non-zero, and achieves an $O(\ln^{3+\epsilon}(N))$ regret in the case that an optimal threshold with full information is $0$ (i.e., an optimal policy is to reject all arrivals), where $N$ is the number of arrivals and $\epsilon>0$.

Chatbots, or bots for short, are multi-modal collaborative assistants that can help people complete useful tasks. Usually, when chatbots are referenced in connection with elections, they often draw negative reactions due to the fear of mis-information and hacking. Instead, in this paper, we explore how chatbots may be used to promote voter participation in vulnerable segments of society like senior citizens and first-time voters. In particular, we build a system that amplifies official information while personalizing it to users' unique needs transparently. We discuss its design, build prototypes with frequently asked questions (FAQ) election information for two US states that are low on an ease-of-voting scale, and report on its initial evaluation in a focus group. Our approach can be a win-win for voters, election agencies trying to fulfill their mandate and democracy at large.

Nostradamus, inspired by the French astrologer and reputed seer, is a detailed study exploring relations between environmental factors and changes in the stock market. In this paper, we analyze associative correlation and causation between environmental elements and stock prices based on the US financial market, global climate trends, and daily weather records to demonstrate significant relationships between climate and stock price fluctuation. Our analysis covers short and long-term rises and dips in company stock performances. Lastly, we take four natural disasters as a case study to observe their effect on the emotional state of people and their influence on the stock market.

With a few exceptions, work in offline reinforcement learning (RL) has so far assumed that there is no confounding. In a classical regression setting, confounders introduce omitted variable bias and inhibit the identification of causal effects. In offline RL, they prevent the identification of a policy's value, and therefore make it impossible to perform policy improvement. Using conventional methods in offline RL in the presence of confounding can therefore not only lead to poor decisions and poor policies, but can also have disastrous effects in applications such as healthcare and education. We provide approaches for both off-policy evaluation (OPE) and local policy optimization in the settings of i.i.d. and global confounders. Theoretical and empirical results confirm the validity and viability of these methods.

The relevance of machine learning (ML) in our daily lives is closely intertwined with its explainability. Explainability can allow end-users to have a transparent and humane reckoning of a ML scheme's capability and utility. It will also foster the user's confidence in the automated decisions of a system. Explaining the variables or features to explain a model's decision is a need of the present times. We could not really find any work, which explains the features on the basis of their class-distinguishing abilities (specially when the real world data are mostly of multi-class nature). In any given dataset, a feature is not equally good at making distinctions between the different possible categorizations (or classes) of the data points. In this work, we explain the features on the basis of their class or category-distinguishing capabilities. We particularly estimate the class-distinguishing capabilities (scores) of the variables for pair-wise class combinations. We validate the explainability given by our scheme empirically on several real-world, multi-class datasets. We further utilize the class-distinguishing scores in a latent feature context and propose a novel decision making protocol. Another novelty of this work lies with a \emph{refuse to render decision} option when the latent variable (of the test point) has a high class-distinguishing potential for the likely classes.

Bayesian causal structure learning aims to learn a posterior distribution over directed acyclic graphs (DAGs), and the mechanisms that define the relationship between parent and child variables. By taking a Bayesian approach, it is possible to reason about the uncertainty of the causal model. The notion of modelling the uncertainty over models is particularly crucial for causal structure learning since the model could be unidentifiable when given only a finite amount of observational data. In this paper, we introduce a novel method to jointly learn the structure and mechanisms of the causal model using Variational Bayes, which we call Variational Bayes-DAG-GFlowNet (VBG). We extend the method of Bayesian causal structure learning using GFlowNets to learn not only the posterior distribution over the structure, but also the parameters of a linear-Gaussian model. Our results on simulated data suggest that VBG is competitive against several baselines in modelling the posterior over DAGs and mechanisms, while offering several advantages over existing methods, including the guarantee to sample acyclic graphs, and the flexibility to generalize to non-linear causal mechanisms.

动态运动原语（DMP）为编码，生成和调整复杂的最终效应轨迹提供了极大的多功能性。 DMP也非常适合从人类演示中学习操纵技巧。但是，DMP的反应性质限制了其用于工具使用和对象操纵任务的适用性，这些任务涉及非全面约束，例如切割手术刀切割或导管转向。在这项工作中，我们通过添加一个耦合项来扩展笛卡尔空间DMP公式，该耦合术语强制执行一组预定义的非独立约束。我们使用udwadia-kalaba方法获得约束强迫项的闭合形式表达式。这种方法提供了一种干净，实用的解决方案，以确保运行时的限制满意度。此外，约束强迫项的提议的分析形式可实现有效的轨迹优化，但受约束。我们通过展示如何从人类示范中学习机器人切割技能来证明这种方法的有用性。

机器学习（ML）是指根据大量数据预测有意义的输出或对复杂系统进行分类的计算机算法。 ML应用于各个领域，包括自然科学，工程，太空探索甚至游戏开发。本文的重点是在化学和生物海洋学领域使用机器学习。在预测全球固定氮水平，部分二氧化碳压力和其他化学特性时，ML的应用是一种有前途的工具。机器学习还用于生物海洋学领域，可从各种图像（即显微镜，流车和视频记录器），光谱仪和其他信号处理技术中检测浮游形式。此外，ML使用其声学成功地对哺乳动物进行了分类，在特定的环境中检测到濒临灭绝的哺乳动物和鱼类。最重要的是，使用环境数据，ML被证明是预测缺氧条件和有害藻华事件的有效方法，这是对环境监测的重要测量。此外，机器学习被用来为各种物种构建许多对其他研究人员有用的数据库，而创建新算法将帮助海洋研究界更好地理解海洋的化学和生物学。

在模拟中测试黑盒感知控制系统面临两个困难。首先，模拟中的感知输入缺乏现实世界传感器输入的保真度。其次，对于合理准确的感知系统，遇到罕见的故障轨迹可能需要进行许多模拟。本文结合了感知误差模型 - 基于传感器的检测系统的替代模型与状态依赖性自适应重要性抽样。这使我们能够有效地评估模拟中现实世界感知控制系统的罕见故障概率。我们使用配备RGB障碍物检测器的自动制动系统进行的实验表明，我们的方法可以使用廉价的模拟来计算准确的故障概率。此外，我们展示了安全指标的选择如何影响能够可靠地采样高概率失败的学习建议分布的过程。