我们研究了离线模仿学习（IL）的问题，在该问题中，代理商旨在学习最佳的专家行为政策，而无需其他在线环境互动。取而代之的是，该代理来自次优行为的补充离线数据集。解决此问题的先前工作要么要求专家数据占据离线数据集的大部分比例，要么需要学习奖励功能并在以后执行离线加强学习（RL）。在本文中，我们旨在解决问题，而无需进行奖励学习和离线RL培训的其他步骤，当时示范包含大量次优数据。基于行为克隆（BC），我们引入了一个额外的歧视者，以区分专家和非专家数据。我们提出了一个合作框架，以增强这两个任务的学习，基于此框架，我们设计了一种新的IL算法，其中歧视者的输出是BC损失的权重。实验结果表明，与基线算法相比，我们提出的算法可获得更高的回报和更快的训练速度。

对比度学习（CL）最近已应用于对抗性学习任务。这种实践将对抗样本视为实例的其他积极观点，并且通过彼此达成最大的协议，可以产生更好的对抗性鲁棒性。但是，由于对抗性扰动可能会导致实例级别的身份混乱，因此这种机制可能存在缺陷，这可能会通过用单独的身份将不同的实例聚集在一起来阻碍CL性能。为了解决这个问题，我们建议在形成鲜明对比时不平等地对待对抗样本，与不对称的Infonce目标（$ a-Infonce $）允许区分对抗样本的考虑。具体而言，对手被视为降低的阳性，会引起较弱的学习信号，或者是与其他负面样本形成较高对比的艰难负面因素。以不对称的方式，可以有效地减轻CL和对抗性学习之间相互冲突目标的不利影响。实验表明，我们的方法始终超过不同鉴定方案的现有对抗性CL方法，而无需额外的计算成本。提出的A-INFONCE也是一种通用形式，可以很容易地扩展到其他CL方法。代码可从https://github.com/yqy2001/a-infonce获得。

离线模仿学习（IL）是从没有奖励标签的专家演示中解决决策问题的强大方法。由于协变量转移，现有的离线IL方法在有限的专家数据下遭受严重的性能变性。但是，包括学习的动力学模型可以潜在地改善专家数据的状态行动空间覆盖范围，但是，它也面临着诸如模型近似/概括/概括性错误和推出数据的次级优势之类的挑战性问题。在本文中，我们提出了基于歧视者指导的基于模型的离线模仿学习（DMIL）框架，该框架引入了一个歧视者，以同时区分模型推出数据的动力学正确性和次优性与真实专家示范。 DMIL采用了一种新颖的合作对抗学习策略，该策略使用歧视者指导和融合了政策和动态模型的学习过程，从而改善了模型性能和鲁棒性。当演示包含大量次优数据时，我们的框架也可以扩展到案例。实验结果表明，与小型数据集下的最新离线IL方法相比，DMIL及其扩展具有出色的性能和鲁棒性。

在没有高保真模拟环境的情况下，学习有效的加强学习（RL）政策可以解决现实世界中的复杂任务。在大多数情况下，我们只有具有简化动力学的不完善的模拟器，这不可避免地导致RL策略学习中的SIM到巨大差距。最近出现的离线RL领域为直接从预先收集的历史数据中学习政策提供了另一种可能性。但是，为了达到合理的性能，现有的离线RL算法需要不切实际的离线数据，并具有足够的州行动空间覆盖范围进行培训。这提出了一个新问题：是否有可能通过在线RL中的不完美模拟器中的离线RL中的有限数据中的学习结合到无限制的探索，以解决两种方法的缺点？在这项研究中，我们提出了动态感知的混合离线和对线增强学习（H2O）框架，以为这个问题提供肯定的答案。 H2O引入了动态感知的政策评估方案，该方案可以自适应地惩罚Q函数在模拟的状态行动对上具有较大的动态差距，同时也允许从固定的现实世界数据集中学习。通过广泛的模拟和现实世界任务以及理论分析，我们证明了H2O与其他跨域在线和离线RL算法相对于其他跨域的表现。 H2O提供了全新的脱机脱机RL范式，该范式可能会阐明未来的RL算法设计，以解决实用的现实世界任务。

In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables. We formalize this scenario by building a new Chinese benchmark KnowSQL consisting of domain-specific questions covering various domains. We then address this problem by presenting formulaic knowledge, rather than by annotating additional data examples. More concretely, we construct a formulaic knowledge bank as a domain knowledge base and propose a framework (ReGrouP) to leverage this formulaic knowledge during parsing. Experiments using ReGrouP demonstrate a significant 28.2% improvement overall on KnowSQL.

The node-place model has been widely used to classify and evaluate transit stations, which sheds light on individual travel behaviors and supports urban planning through effectively integrating land use and transportation development. This article adapts this model to investigate whether and how node, place, and mobility would be associated with the transmission risks and presences of the local COVID-19 cases in a city. Similar studies on the model and its relevance to COVID-19, according to our knowledge, have not been undertaken before. Moreover, the unique metric drawn from detailed visit history of the infected, i.e., the COVID-19 footprints, is proposed and exploited. This study then empirically uses the adapted model to examine the station-level factors affecting the local COVID-19 footprints. The model accounts for traditional measures of the node and place as well as actual human mobility patterns associated with the node and place. It finds that stations with high node, place, and human mobility indices normally have more COVID-19 footprints in proximity. A multivariate regression is fitted to see whether and to what degree different indices and indicators can predict the COVID-19 footprints. The results indicate that many of the place, node, and human mobility indicators significantly impact the concentration of COVID-19 footprints. These are useful for policy-makers to predict and monitor hotspots for COVID-19 and other pandemics transmission.

Text-to-SQL semantic parsing is an important NLP task, which greatly facilitates the interaction between users and the database and becomes the key component in many human-computer interaction systems. Much recent progress in text-to-SQL has been driven by large-scale datasets, but most of them are centered on English. In this work, we present MultiSpider, the largest multilingual text-to-SQL dataset which covers seven languages (English, German, French, Spanish, Japanese, Chinese, and Vietnamese). Upon MultiSpider, we further identify the lexical and structural challenges of text-to-SQL (caused by specific language properties and dialect sayings) and their intensity across different languages. Experimental results under three typical settings (zero-shot, monolingual and multilingual) reveal a 6.1% absolute drop in accuracy in non-English languages. Qualitative and quantitative analyses are conducted to understand the reason for the performance drop of each language. Besides the dataset, we also propose a simple schema augmentation framework SAVe (Schema-Augmentation-with-Verification), which significantly boosts the overall performance by about 1.8% and closes the 29.5% performance gap across languages.

Practical applications employing deep learning must guarantee inference quality. However, we found that the inference quality of state-of-the-art and state-of-the-practice in practical applications has a long tail distribution. In the real world, many tasks have strict requirements for the quality of deep learning inference, such as safety-critical and mission-critical tasks. The fluctuation of inference quality seriously affects its practical applications, and the quality at the tail may lead to severe consequences. State-of-the-art and state-of-the-practice with outstanding inference quality designed and trained under loose constraints still have poor inference quality under constraints with practical application significance. On the one hand, the neural network models must be deployed on complex systems with limited resources. On the other hand, safety-critical and mission-critical tasks need to meet more metric constraints while ensuring high inference quality. We coin a new term, ``tail quality,'' to characterize this essential requirement and challenge. We also propose a new metric, ``X-Critical-Quality,'' to measure the inference quality under certain constraints. This article reveals factors contributing to the failure of using state-of-the-art and state-of-the-practice algorithms and systems in real scenarios. Therefore, we call for establishing innovative methodologies and tools to tackle this enormous challenge.

Previous computation models either have equivalent abilities in representing all computations but fail to provide primitive operators for programming complex algorithms or lack generalized expression ability to represent newly-added computations. This article presents a unified computation model with generalized expression ability and a concise set of primitive operators for programming high-level algorithms. We propose a unified data abstraction -- Tensor of List, and offer a unified computation model based on Tensor of List, which we call the ToL model (in short, ToL). ToL introduces five atomic computations that can represent any elementary computation by finite composition, ensured with strict formal proof. Based on ToL, we design a pure-functional language -- ToLang. ToLang provides a concise set of primitive operators that can be used to program complex big data and AI algorithms. Our evaluations show ToL has generalized expression ability and a built-in performance indicator, born with a strictly defined computation metric -- elementary operation count (EOPs), consistent with FLOPs within a small error range.

Medical Visual Question Answering (Medical-VQA) aims to answer clinical questions regarding radiology images, assisting doctors with decision-making options. Nevertheless, current Medical-VQA models learn cross-modal representations through residing vision and texture encoders in dual separate spaces, which lead to indirect semantic alignment. In this paper, we propose UnICLAM, a Unified and Interpretable Medical-VQA model through Contrastive Representation Learning with Adversarial Masking. Specifically, to learn an aligned image-text representation, we first establish a unified dual-stream pre-training structure with the gradually soft-parameter sharing strategy. Technically, the proposed strategy learns a constraint for the vision and texture encoders to be close in a same space, which is gradually loosened as the higher number of layers. Moreover, for grasping the semantic representation, we extend the unified Adversarial Masking data augmentation strategy to the contrastive representation learning of vision and text in a unified manner, alleviating the meaningless of the commonly used random mask. Concretely, while the encoder training minimizes the distance between the original feature and the masking feature, the adversarial masking model keeps adversarial learning to conversely maximize the distance. Furthermore, we also intuitively take a further exploration of the unified adversarial masking strategy, which improves the potential ante-hoc interpretability with remarkable performance and efficiency. Experimental results on VQA-RAD and SLAKE public benchmarks demonstrate that UnICLAM outperforms the existing 11 state-of-the-art Medical-VQA models. More importantly, we make an additional discussion about the performance of UnICLAM in diagnosing heart failure, verifying that UnICLAM exhibits superior few-shot adaption performance in practical disease diagnosis.