合成控制方法开创了一类强大的数据驱动技术，以估算捐助单元的单位的反事实现实。从本质上讲，该技术涉及在干预前时期安装的线性模型，该模型结合了供体结果以产生反事实。但是，使用时间不足的权重在每个时间实例上线性组合空间信息都无法捕获重要的单位间和单位内的时间上下文以及真实数据的复杂非线性动力学。相反，我们提出了一种在干预开始之前使用局部时空信息作为估计反事实序列的有希望的方法的方法。为此，我们建议了一个变压器模型，该模型利用特定的位置嵌入，修改的解码器掩模以及一项新的预训练任务来执行时空序列到序列建模。我们对合成数据的实验证明了我们方法在典型的小型供体池设置中的功效及其对噪声的稳健性。我们还通过模拟全州范围的公共卫生政策来评估其有效性，对哮喘药物进行支持，以支持随机对照试验的疾病，以及针对弗里德雷希共济失调的患者改进的医疗干预措施，从而在人口和患者水平上产生可行的医疗保健见解，以评估其有效性。临床决策并促进个性化治疗。

Recent work has shown that large language models are capable of generating natural language reasoning steps or Chains-of-Thoughts (CoT) to answer a multi-step question when prompted to do so. This is insufficient, however, when the necessary knowledge is not available or up-to-date within a model's parameters. A straightforward approach to address this is to retrieve text from an external knowledge source using the question as a query and prepend it as context to the model's input. This, however, is also insufficient for multi-step QA where \textit{what to retrieve} depends on \textit{what has already been derived}. To address this issue we propose IRCoT, a new approach that interleaves retrieval with CoT for multi-step QA, guiding the retrieval with CoT and in turn using retrieved results to improve CoT. Our experiments with GPT3 show substantial improvements in retrieval (up to 22 points) and downstream QA (up to 16 points) over the baselines on four datasets: HotpotQA, 2WikiMultihopQA, MuSiQue, and IIRC. Notably, our method also works well for much smaller models such as T5-Flan-large (0.7B) without any additional training.

Knowledge about outcomes is critical for complex event understanding but is hard to acquire. We show that by pre-identifying a participant in a complex event, crowd workers are able to (1) infer the collective impact of salient events that make up the situation, (2) annotate the volitional engagement of participants in causing the situation, and (3) ground the outcome of the situation in state changes of the participants. By creating a multi-step interface and a careful quality control strategy, we collect a high quality annotated dataset of 8K short newswire narratives and ROCStories with high inter-annotator agreement (0.74-0.96 weighted Fleiss Kappa). Our dataset, POQue (Participant Outcome Questions), enables the exploration and development of models that address multiple aspects of semantic understanding. Experimentally, we show that current language models lag behind human performance in subtle ways through our task formulations that target abstract and specific comprehension of a complex event, its outcome, and a participant's influence over the event culmination.

Explainability has been widely stated as a cornerstone of the responsible and trustworthy use of machine learning models. With the ubiquitous use of Deep Neural Network (DNN) models expanding to risk-sensitive and safety-critical domains, many methods have been proposed to explain the decisions of these models. Recent years have also seen concerted efforts that have shown how such explanations can be distorted (attacked) by minor input perturbations. While there have been many surveys that review explainability methods themselves, there has been no effort hitherto to assimilate the different methods and metrics proposed to study the robustness of explanations of DNN models. In this work, we present a comprehensive survey of methods that study, understand, attack, and defend explanations of DNN models. We also present a detailed review of different metrics used to evaluate explanation methods, as well as describe attributional attack and defense methods. We conclude with lessons and take-aways for the community towards ensuring robust explanations of DNN model predictions.

Estimating treatment effects from observational data is a central problem in causal inference. Methods to solve this problem exploit inductive biases and heuristics from causal inference to design multi-head neural network architectures and regularizers. In this work, we propose to use neurosymbolic program synthesis, a data-efficient, and interpretable technique, to solve the treatment effect estimation problem. We theoretically show that neurosymbolic programming can solve the treatment effect estimation problem. By designing a Domain Specific Language (DSL) for treatment effect estimation problem based on the inductive biases used in literature, we argue that neurosymbolic programming is a better alternative to treatment effect estimation than traditional methods. Our empirical study reveals that our method, which implicitly encodes inductive biases in a DSL, achieves better performance on benchmark datasets than the state-of-the-art methods.

A machine learning model, under the influence of observed or unobserved confounders in the training data, can learn spurious correlations and fail to generalize when deployed. For image classifiers, augmenting a training dataset using counterfactual examples has been empirically shown to break spurious correlations. However, the counterfactual generation task itself becomes more difficult as the level of confounding increases. Existing methods for counterfactual generation under confounding consider a fixed set of interventions (e.g., texture, rotation) and are not flexible enough to capture diverse data-generating processes. Given a causal generative process, we formally characterize the adverse effects of confounding on any downstream tasks and show that the correlation between generative factors (attributes) can be used to quantitatively measure confounding between generative factors. To minimize such correlation, we propose a counterfactual generation method that learns to modify the value of any attribute in an image and generate new images given a set of observed attributes, even when the dataset is highly confounded. These counterfactual images are then used to regularize the downstream classifier such that the learned representations are the same across various generative factors conditioned on the class label. Our method is computationally efficient, simple to implement, and works well for any number of generative factors and confounding variables. Our experimental results on both synthetic (MNIST variants) and real-world (CelebA) datasets show the usefulness of our approach.

从有限的资源中获得最大收益可以进步自然语言处理（NLP）研究和实践，同时保守资源。这些资源可能是数据，时间，存储或能源。NLP的最新工作从缩放率产生了有趣的结果。但是，仅使用比例来改善结果意味着资源消耗也会扩展。这种关系激发了对有效方法的研究，这些方法需要更少的资源才能获得相似的结果。这项调查涉及NLP效率的方法和发现，旨在指导该领域的新研究人员并激发新方法的发展。

本文提出了一个基于混合融合的多模式情感识别系统，该系统将语音话语和相应图像描绘的情绪分类为离散类。已经开发了一种新的可解释性技术，以确定重要的语音和图像特征，从而预测特定的情感类别。拟议的系统的体系结构是通过大量消融研究确定的。它融合了语音和图像特征，然后结合了语音，图像和中间融合输出。提出的可解释性技术结合了划分和征服方法，以计算表示每个语音和图像特征的重要性的刻薄值。我们还构建了一个大规模数据集（IIT-R较小的数据集），包括语音话语，相应的图像和班级标签，即“愤怒”，“快乐”，“仇恨”和“悲伤”。拟议的系统已达到83.29％的情绪识别精度。提出的系统的增强性能提倡利用多种模式中的互补信息来识别情绪的重要性。

本文提出了一个多模式的情感识别系统，即视觉口语文本添加剂网（Vista Net），以将包含图像，语音和文本的多模式输入反映的情绪分类为离散类。还开发了一种新的可解释性技术，即K平均添加剂解释（KAAP），以确定重要的视觉，口语和文本特征，从而预测特定的情感类别。 Vista Net使用早期和晚期融合的混合体从图像，语音和文本方式融合信息。它会自动调整其中间输出的权重，同时在不干预的情况下计算加权平均值。 KAAP技术计算每种方式和相应特征在预测特定情绪类别的贡献。为了减轻带有离散情绪类别标记的多模式情感数据集的不足，我们构建了一个大规模的IIT-R MMEMOREC数据集，该数据集由现实生活中的图像，相应的语音和文本和情感标签（“愤怒，'快乐，''happy，''快乐，'' “恨，”和“悲伤”。）。 Vista Net在考虑图像，语音和文本方式上导致了95.99％的情绪识别精度，这比考虑任何一种或两种方式的输入的表现要好。

剪切粘度虽然是所有液体的基本特性，但在计算上估计分子动力学模拟的计算昂贵。最近，机器学习（ML）方法已被用于在许多情况下增强分子模拟，从而显示出以相对廉价的方式估算粘度的希望。但是，ML方法面临重大挑战，例如当数据集的大小很小时，粘度也很小。在这项工作中，我们训练多个ML模型，以预测Lennard-Jones（LJ）流体的剪切粘度，特别强调解决由小型数据集引起的问题。具体而言，研究了与模型选择，绩效估计和不确定性定量有关的问题。首先，我们表明使用单个看不见的数据集的广泛使用的性能估计步骤显示了小数据集的广泛可变性。在这种情况下，可以使用交叉验证（CV）选择超参数（模型选择）的常见实践，以估算概括误差（性能估计）。我们比较了两个简单的简历程序，以便他们同时选择模型选择和性能估计的能力，并发现基于K折CV的过程显示出较低的误差估计差异。我们讨论绩效指标在培训和评估中的作用。最后，使用高斯工艺回归（GPR）和集合方法来估计单个预测的不确定性。 GPR的不确定性估计还用于构建适用性域，使用ML模型对本工作中生成的另一个小数据集提供了更可靠的预测。总体而言，这项工作中规定的程序共同导致了针对小型数据集的强大ML模型。