数据异构联合学习（FL）系统遭受了两个重要的收敛误差来源：1）客户漂移错误是由于在客户端执行多个局部优化步骤而引起的，以及2）部分客户参与错误，这是一个事实，仅一小部分子集边缘客户参加每轮培训。我们发现其中，只有前者在文献中受到了极大的关注。为了解决这个问题，我们提出了FedVarp，这是在服务器上应用的一种新颖的差异算法，它消除了由于部分客户参与而导致的错误。为此，服务器只是将每个客户端的最新更新保持在内存中，并将其用作每回合中非参与客户的替代更新。此外，为了减轻服务器上的内存需求，我们提出了一种新颖的基于聚类的方差降低算法clusterfedvarp。与以前提出的方法不同，FedVarp和ClusterFedVarp均不需要在客户端上进行其他计算或其他优化参数的通信。通过广泛的实验，我们表明FedVarp优于最先进的方法，而ClusterFedVarp实现了与FedVarp相当的性能，并且记忆要求较少。

Dense retrievers have made significant strides in obtaining state-of-the-art results on text retrieval and open-domain question answering (ODQA). Yet most of these achievements were made possible with the help of large annotated datasets, unsupervised learning for dense retrieval models remains an open problem. In this work, we explore two categories of methods for creating pseudo query-document pairs, named query extraction (QExt) and transferred query generation (TQGen), to augment the retriever training in an annotation-free and scalable manner. Specifically, QExt extracts pseudo queries by document structures or selecting salient random spans, and TQGen utilizes generation models trained for other NLP tasks (e.g., summarization) to produce pseudo queries. Extensive experiments show that dense retrievers trained with individual augmentation methods can perform comparably well with multiple strong baselines, and combining them leads to further improvements, achieving state-of-the-art performance of unsupervised dense retrieval on both BEIR and ODQA datasets.

This paper introduces the shared task of summarizing documents in several creative domains, namely literary texts, movie scripts, and television scripts. Summarizing these creative documents requires making complex literary interpretations, as well as understanding non-trivial temporal dependencies in texts containing varied styles of plot development and narrative structure. This poses unique challenges and is yet underexplored for text summarization systems. In this shared task, we introduce four sub-tasks and their corresponding datasets, focusing on summarizing books, movie scripts, primetime television scripts, and daytime soap opera scripts. We detail the process of curating these datasets for the task, as well as the metrics used for the evaluation of the submissions. As part of the CREATIVESUMM workshop at COLING 2022, the shared task attracted 18 submissions in total. We discuss the submissions and the baselines for each sub-task in this paper, along with directions for facilitating future work in the field.

近年来，机器学习（ML）已更加严重地依赖人群工人来构建更大的数据集以及解决需要人类互动或判断的研究问题。由于人群工人执行的各种任务以及使用了由此产生的数据集的无数方式，因此很难确定何时最好将这些人视为工人，而不是人类受试者。这些困难因政策矛盾而加剧了，一些机构和研究人员将所有ML人群视为人类主题研究，而其他机构认为ML人群工人很少构成人类的受试者。此外，很少有涉及人群工作的ML论文提到IRB的监督，这提出了许多可能不符合道德和监管要求的前景。在本文中，我们专注于自然语言处理的研究，以研究众包研究的适当指定以及ML研究对研究监督所带来的独特挑战。至关重要的是，根据美国的共同规则，这些判断取决于“关于内在”的确定，即收集到的数据与谁以及（或什么）有关的人（或什么）。我们强调了ML提出的两个挑战：（1）相同的工人可以担任多个角色并提供多种信息； （2）与生命科学和社会科学相比，ML研究倾向于接受动态的工作流程，很少有人指出研究问题，而数据共享为未来的研究打开了大门，以提出有关原始研究不同目标的问题。特别是，我们的分析在共同规则中暴露了潜在的漏洞，研究人员可以通过将数据收集和分析分为不同的研究来避免研究伦理的监督。我们提供了一些政策建议来解决这些问题。

Learning policies that effectively utilize language instructions in complex, multi-task environments is an important problem in sequential decision-making. While it is possible to condition on the entire language instruction directly, such an approach could suffer from generalization issues. In our work, we propose \emph{Learning Interpretable Skill Abstractions (LISA)}, a hierarchical imitation learning framework that can learn diverse, interpretable primitive behaviors or skills from language-conditioned demonstrations to better generalize to unseen instructions. LISA uses vector quantization to learn discrete skill codes that are highly correlated with language instructions and the behavior of the learned policy. In navigation and robotic manipulation environments, LISA outperforms a strong non-hierarchical Decision Transformer baseline in the low data regime and is able to compose learned skills to solve tasks containing unseen long-range instructions. Our method demonstrates a more natural way to condition on language in sequential decision-making problems and achieve interpretable and controllable behavior with the learned skills.

在许多顺序决策问题（例如，机器人控制，游戏播放，顺序预测），人类或专家数据可用包含有关任务的有用信息。然而，来自少量专家数据的模仿学习（IL）可能在具有复杂动态的高维环境中具有挑战性。行为克隆是一种简单的方法，由于其简单的实现和稳定的收敛而被广泛使用，但不利用涉及环境动态的任何信息。由于对奖励和政策近似器或偏差，高方差梯度估计器，难以在实践中难以在实践中努力训练的许多现有方法。我们介绍了一种用于动态感知IL的方法，它通过学习单个Q函数来避免对抗训练，隐含地代表奖励和策略。在标准基准测试中，隐式学习的奖励显示与地面真实奖励的高正面相关性，说明我们的方法也可以用于逆钢筋学习（IRL）。我们的方法，逆软Q学习（IQ-Learn）获得了最先进的结果，在离线和在线模仿学习设置中，显着优于现有的现有方法，这些方法都在所需的环境交互和高维空间中的可扩展性中，通常超过3倍。

最近，我们已经看到了在社交网络和智能手机上的通信使用文本聊天的增加。这尤其涉及使用印度英语代码混合文本，其中包含在英语词汇中无法识别的单词。我们已经致力于在这些混合数据中检测到情绪，并将句子的句子分类为生气，恐惧，快乐或悲伤。我们使用了最先进的自然语言处理模型，并将其在包括该混合数据中的句子的数据集上的性能。将数据集收集并从来源注释，然后用于培训模型。

The majority of available text summarization datasets include short-form source documents that lack long-range causal and temporal dependencies, and often contain strong layout and stylistic biases. While relevant, such datasets will offer limited challenges for future generations of text summarization systems. We address these issues by introducing BookSum, a collection of datasets for long-form narrative summarization. Our dataset covers source documents from the literature domain, such as novels, plays and stories, and includes highly abstractive, human written summaries on three levels of granularity of increasing difficulty: paragraph-, chapter-, and book-level. The domain and structure of our dataset poses a unique set of challenges for summarization systems, which include: processing very long documents, non-trivial causal and temporal dependencies, and rich discourse structures. To facilitate future work, we trained and evaluated multiple extractive and abstractive summarization models as baselines for our dataset.

3D object detection is an essential task in autonomous driving. Recent techniques excel with highly accurate detection rates, provided the 3D input data is obtained from precise but expensive LiDAR technology. Approaches based on cheaper monocular or stereo imagery data have, until now, resulted in drastically lower accuracies -a gap that is commonly attributed to poor image-based depth estimation. However, in this paper we argue that it is not the quality of the data but its representation that accounts for the majority of the difference. Taking the inner workings of convolutional neural networks into consideration, we propose to convert image-based depth maps to pseudo-LiDAR representations -essentially mimicking the LiDAR signal. With this representation we can apply different existing LiDAR-based detection algorithms. On the popular KITTI benchmark, our approach achieves impressive improvements over the existing state-of-the-art in image-based performance -raising the detection accuracy of objects within the 30m range from the previous state-of-the-art of 22% to an unprecedented 74%. At the time of submission our algorithm holds the highest entry on the KITTI 3D object detection leaderboard for stereo-image-based approaches. Our code is publicly available at https: //github.com/mileyan/pseudo_lidar.