Abusive language is a concerning problem in online social media. Past research on detecting abusive language covers different platforms, languages, demographies, etc. However, models trained using these datasets do not perform well in cross-domain evaluation settings. To overcome this, a common strategy is to use a few samples from the target domain to train models to get better performance in that domain (cross-domain few-shot training). However, this might cause the models to overfit the artefacts of those samples. A compelling solution could be to guide the models toward rationales, i.e., spans of text that justify the text's label. This method has been found to improve model performance in the in-domain setting across various NLP tasks. In this paper, we propose RAFT (Rationale Adaptor for Few-shoT classification) for abusive language detection. We first build a multitask learning setup to jointly learn rationales, targets, and labels, and find a significant improvement of 6% macro F1 on the rationale detection task over training solely rationale classifiers. We introduce two rationale-integrated BERT-based architectures (the RAFT models) and evaluate our systems over five different abusive language datasets, finding that in the few-shot classification setting, RAFT-based models outperform baseline models by about 7% in macro F1 scores and perform competitively to models finetuned on other source domains. Furthermore, RAFT-based models outperform LIME/SHAP-based approaches in terms of plausibility and are close in performance in terms of faithfulness.

随着共同群众在社交媒体中的参与不断上升，政策制定者/记者在社交媒体上进行在线民意调查以了解人们在特定地点的政治倾向是越来越普遍的。这里的警告是，只有有影响力的人才能进行这样的在线民意调查并大规模伸展。此外，在这种情况下，选民的分配是不可控制的，实际上可能是有偏见的。另一方面，如果我们可以通过社交媒体解释公开可用的数据来探究用户的政治倾向，我们将能够对调查人群有可控的见解，保持低调的成本，并在没有公开数据的情况下收集公开可用的数据涉及有关人员。因此，我们引入了一个自我牵键的半监督框架，以进一步进一步实现这一目标。我们模型的优点是它既不需要大量的培训数据，也不需要存储社交网络参数。然而，它在没有带注释的数据的情况下达到了93.7 \％的精度。此外，每个课程只有几个注释的示例可以实现竞争性能。我们发现，即使在资源约束的设置中，该模型也是高效的，并且从其预测中得出的见解与手动调查结果相匹配时，将其应用于不同的现实生活中。

人口统计学分类对于推荐系统的公平评估或测量在线网络和投票系统中的意外偏见至关重要。教育和政治等重要领域经常为社会平等的未来奠定基础，需要审查设计政策，这些政策可以更好地促进该国人口不平衡的人口分布限制的资源分配平等。我们收集三个公开可用的数据集，以培训性别和种姓分类领域的最先进的分类器。我们在印度背景下对模型进行训练，那里的同名可以拥有不同的造型惯例（一种州的Jolly Abraham/Kumar Abhishikta可以写为Abraham Jolly/Abishikta Kumar）。最后，我们还执行跨测试（在不同数据集上的培训和测试）以了解上述模型的功效。我们还对预测模型执行错误分析。最后，我们试图评估现有印度系统的偏见作为案例研究，并找到一些在性别和种姓层面的次大陆的复杂人口布局中表现出的有趣模式。

今天，参加在线论坛上的讨论非常普遍，这些讨论已经开始对在线用户的整体意见产生强大的影响。 Naturally, twisting the flow of the argument can have a strong impact on the minds of naive users, which in the long run might have socio-political ramifications, for example, winning an election or spreading targeted misinformation.因此，这些平台可能非常容易受到恶意玩家的影响，他们可能会单独采取行动，也可能是繁殖谬误的争论，并动机促进公众舆论。 AD HOMINEM论点是此类谬论中最有效的形式之一。尽管是一个简单的谬论，但它足够有效，可以在离线世界中进行公开辩论，并且可以用作阻止诽谤反对派声音的先驱。在这项工作中，我们迈出了第一步，以阐明野外Ad Hominem谬论的使用。首先，我们建立了一个具有很高准确性的强大AD HOMINEM探测器（F1超过83％，对先前的工作显示出显着改善），即使对于注释的实例构成很小一部分的数据集也是如此。然后，我们在从在线辩论论坛中收集的265k参数（创建者）中使用了我们的检测器。我们的众包调查验证了我们对创建ebate数据的野外预测（94％与手动注释相匹配）。我们的分析表明，令人惊讶的31.23％的创建ebate内容包含AD HOMINEM谬论，并且一群高度活跃的用户的同类发表了更大的AD AD本人，以抑制相反的观点。然后，我们的时间分析表明，自2016年美国总统大选以来，AD HOMINEM论点的使用量显着增加，不仅是政治等主题，而且对于科学和法律。最后，我们讨论了我们的工作的重要意义，以检测和防御AD HOMINEM谬论。

时间轴提供了最有效的方法之一，可以看到一段时间内发生的重要历史事实，从而呈现出从文本形式阅读等效信息的见解。通过利用生成的对抗性学习进行重要的句子分类，并通过吸收基于知识的标签来改善事件核心分辨率的性能，我们从多个（历史）文本文档中引入了两个分阶段的事件时间表生成的系统。我们在两个手动注释的历史文本文档上演示了我们的结果。我们的结果对历史学家，推进历史研究以及理解一个国家的社会政治格局的研究对历史学家来说非常有帮助。

自动面检测等计算机视觉应用用于各种目的，从解锁智能设备到跟踪监视的潜在感兴趣的人。这些申请的审计透露，他们倾向于对少数民族群体偏见，导致不公平和关于社会和政治结果。尽管随着时间的推移，但这些偏差尚未完全减轻，但实际上已经增加了年龄预测等任务。虽然这些系统审核了基准数据集，但有必要评估其对抗性投入的鲁棒性。在这项工作中，我们在多个系统和数据集上进行广泛的对手审核，并进行了许多关于观察 - 从以前的审计以来的一些任务对一些任务进行了准确性。虽然仍然对多个数据集的少数群体的个体仍然存在偏差，但更令人担忧的观察是这些偏差倾向于对少数群体的对抗意义进行过度发音。我们讨论了鉴于这些观察结果更广泛的社会影响以及关于如何共同应对这个问题的建议。

We consider the problem of constructing minimax rate-optimal estimators for a doubly robust nonparametric functional that has witnessed applications across the causal inference and conditional independence testing literature. Minimax rate-optimal estimators for such functionals are typically constructed through higher-order bias corrections of plug-in and one-step type estimators and, in turn, depend on estimators of nuisance functions. In this paper, we consider a parallel question of interest regarding the optimality and/or sub-optimality of plug-in and one-step bias-corrected estimators for the specific doubly robust functional of interest. Specifically, we verify that by using undersmoothing and sample splitting techniques when constructing nuisance function estimators, one can achieve minimax rates of convergence in all H\"older smoothness classes of the nuisance functions (i.e. the propensity score and outcome regression) provided that the marginal density of the covariates is sufficiently regular. Additionally, by demonstrating suitable lower bounds on these classes of estimators, we demonstrate the necessity to undersmooth the nuisance function estimators to obtain minimax optimal rates of convergence.

Learning policies from fixed offline datasets is a key challenge to scale up reinforcement learning (RL) algorithms towards practical applications. This is often because off-policy RL algorithms suffer from distributional shift, due to mismatch between dataset and the target policy, leading to high variance and over-estimation of value functions. In this work, we propose variance regularization for offline RL algorithms, using stationary distribution corrections. We show that by using Fenchel duality, we can avoid double sampling issues for computing the gradient of the variance regularizer. The proposed algorithm for offline variance regularization (OVAR) can be used to augment any existing offline policy optimization algorithms. We show that the regularizer leads to a lower bound to the offline policy optimization objective, which can help avoid over-estimation errors, and explains the benefits of our approach across a range of continuous control domains when compared to existing state-of-the-art algorithms.

Search and Rescue (SAR) missions in remote environments often employ autonomous multi-robot systems that learn, plan, and execute a combination of local single-robot control actions, group primitives, and global mission-oriented coordination and collaboration. Often, SAR coordination strategies are manually designed by human experts who can remotely control the multi-robot system and enable semi-autonomous operations. However, in remote environments where connectivity is limited and human intervention is often not possible, decentralized collaboration strategies are needed for fully-autonomous operations. Nevertheless, decentralized coordination may be ineffective in adversarial environments due to sensor noise, actuation faults, or manipulation of inter-agent communication data. In this paper, we propose an algorithmic approach based on adversarial multi-agent reinforcement learning (MARL) that allows robots to efficiently coordinate their strategies in the presence of adversarial inter-agent communications. In our setup, the objective of the multi-robot team is to discover targets strategically in an obstacle-strewn geographical area by minimizing the average time needed to find the targets. It is assumed that the robots have no prior knowledge of the target locations, and they can interact with only a subset of neighboring robots at any time. Based on the centralized training with decentralized execution (CTDE) paradigm in MARL, we utilize a hierarchical meta-learning framework to learn dynamic team-coordination modalities and discover emergent team behavior under complex cooperative-competitive scenarios. The effectiveness of our approach is demonstrated on a collection of prototype grid-world environments with different specifications of benign and adversarial agents, target locations, and agent rewards.

In the process of materials discovery, chemists currently need to perform many laborious, time-consuming, and often dangerous lab experiments. To accelerate this process, we propose a framework for robots to assist chemists by performing lab experiments autonomously. The solution allows a general-purpose robot to perform diverse chemistry experiments and efficiently make use of available lab tools. Our system can load high-level descriptions of chemistry experiments, perceive a dynamic workspace, and autonomously plan the required actions and motions to perform the given chemistry experiments with common tools found in the existing lab environment. Our architecture uses a modified PDDLStream solver for integrated task and constrained motion planning, which generates plans and motions that are guaranteed to be safe by preventing collisions and spillage. We present a modular framework that can scale to many different experiments, actions, and lab tools. In this work, we demonstrate the utility of our framework on three pouring skills and two foundational chemical experiments for materials synthesis: solubility and recrystallization. More experiments and updated evaluations can be found at https://ac-rad.github.io/arc-icra2023.