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.

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.

Informed consent has become increasingly salient for data privacy and its regulation. Entities from governments to for-profit companies have addressed concerns about data privacy with policies that enumerate the conditions for personal data storage and transfer. However, increased enumeration of and transparency in data privacy policies has not improved end-users' comprehension of how their data might be used: not only are privacy policies written in legal language that users may struggle to understand, but elements of these policies may compose in such a way that the consequences of the policy are not immediately apparent. We present a framework that uses Answer Set Programming (ASP) -- a type of logic programming -- to formalize privacy policies. Privacy policies thus become constraints on a narrative planning space, allowing end-users to forward-simulate possible consequences of the policy in terms of actors having roles and taking actions in a domain. We demonstrate through the example of the Health Insurance Portability and Accountability Act (HIPAA) how to use the system in various ways, including asking questions about possibilities and identifying which clauses of the law are broken by a given sequence of events.

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.

韦伯·费希纳（Weber Fechner）的心理物理学定律观察到人类的感知在刺激中是对数。我们提出了一种算法，将Weber Fechner定律纳入机器学习的损失功能，并使用该算法来增强深度学习网络的性能。

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。

通过将云资源转换为用户的邻近来减轻云计算所拥有的限制来引入雾计算。雾环境使其有限的资源可用于大量用户部署其无服务器的应用程序，由多个无服务器功能组成。引入迷雾环境背后的主要意图是通过其有限的资源来满足延迟和位置敏感无服务器应用程序的需求。最近的研究主要侧重于将最大资源分配给来自FOG节点的这些应用程序，而不是充分利用云环境。这引入了在将资源提供给最大连接用户的负面影响。为了解决此问题，在本文中，我们调查了用户请求的最佳百分比，该请求应由雾和云实现。因此，我们提出了Def-Driel，系统地部署了使用深度增强学习的雾和云环境中无服务器功能，使用若干现实生活参数，例如来自附近FOG节点，用户的优先级的用户的距离和延迟，与最近的相关算法相比，无服务器应用程序的优先级及其资源需求等。从模拟和比较结果，可以清楚地观察到其对其他算法的优势及其对现实生活场景的适用性。