我们研究了对识别的非唯一麻烦的线性功能的通用推断，该功能定义为未识别条件矩限制的解决方案。这个问题出现在各种应用中，包括非参数仪器变量模型，未衡量的混杂性下的近端因果推断以及带有阴影变量的丢失 - 与随机数据。尽管感兴趣的线性功能（例如平均治疗效应）在适当的条件下是可以识别出的，但令人讨厌的非独家性对统计推断构成了严重的挑战，因为在这种情况下，常见的滋扰估计器可能是不稳定的，并且缺乏固定限制。在本文中，我们提出了对滋扰功能的受惩罚的最小估计器，并表明它们在这种挑战性的环境中有效推断。提出的滋扰估计器可以适应灵活的功能类别，重要的是，无论滋扰是否是唯一的，它们都可以融合到由惩罚确定的固定限制。我们使用受惩罚的滋扰估计器来形成有关感兴趣的线性功能的依据估计量，并在通用高级条件下证明其渐近正态性，这提供了渐近有效的置信区间。

非政策评估和学习（OPE/L）使用离线观察数据来做出更好的决策，这对于在线实验有限的应用至关重要。但是，完全取决于记录的数据，OPE/L对环境分布的变化很敏感 - 数据生成环境和部署策略的差异。 \ citet {si2020distributional}提议的分布在稳健的OPE/L（Drope/L）解决此问题，但该提案依赖于逆向权重，如果估计错误和遗憾，如果倾向是非参数估计的，即使其差异是次级估计，即使是次级估计的，其估计错误和遗憾将降低。对于标准的，非体，OPE/L，这是通过双重鲁棒（DR）方法来解决的，但它们并不自然地扩展到更复杂的drop/l，涉及最糟糕的期望。在本文中，我们提出了具有KL-Divergence不确定性集的DROPE/L的第一个DR算法。为了进行评估，我们提出了局部双重稳健的drope（LDR $^2 $ ope），并表明它在弱产品速率条件下实现了半摩托效率。多亏了本地化技术，LDR $^2 $ OPE仅需要安装少量回归，就像标准OPE的DR方法一样。为了学习，我们提出了连续的双重稳健下降（CDR $^2 $ opl），并表明，在涉及连续回归的产品速率条件下，它具有$ \ Mathcal {o} \ left的快速后悔率（n^） {-1/2} \ right）$即使未知的倾向是非参数估计的。我们从经验上验证了模拟中的算法，并将结果进一步扩展到一般$ f $ divergence的不确定性集。

当并非观察到所有混杂因子并获得负面对照时，我们研究因果参数的估计。最近的工作表明，这些方法如何通过两个所谓的桥梁函数来实现识别和有效估计。在本文中，我们使用阴性对照来应对因果推断的主要挑战：这些桥梁功能的识别和估计。先前的工作依赖于这些功能的完整性条件，以识别因果参数并在估计中需要进行独特性假设，并且还集中于桥梁函数的参数估计。相反，我们提供了一种新的识别策略，以避免完整性条件。而且，我们根据最小学习公式为这些功能提供新的估计量。这些估计值适合通用功能类别，例如重现Hilbert空间和神经网络。我们研究了有限样本收敛的结果，既可以估计桥梁功能本身，又要在各种假设组合下对因果参数进行最终估计。我们尽可能避免桥梁上的独特条件。

In many investigations, the primary outcome of interest is difficult or expensive to collect. Examples include long-term health effects of medical interventions, measurements requiring expensive testing or follow-up, and outcomes only measurable on small panels as in marketing. This reduces effective sample sizes for estimating the average treatment effect (ATE). However, there is often an abundance of observations on surrogate outcomes not of primary interest, such as short-term health effects or online-ad click-through. We study the role of such surrogate observations in the efficient estimation of treatment effects. To quantify their value, we derive the semiparametric efficiency bounds on ATE estimation with and without the presence of surrogates and several intermediary settings. The difference between these characterizes the efficiency gains from optimally leveraging surrogates. We study two regimes: when the number of surrogate observations is comparable to primary-outcome observations and when the former dominates the latter. We take an agnostic missing-data approach circumventing strong surrogate conditions previously assumed. To leverage surrogates' efficiency gains, we develop efficient ATE estimation and inference based on flexible machine-learning estimates of nuisance functions appearing in the influence functions we derive. We empirically demonstrate the gains by studying the long-term earnings effect of job training.

我们考虑在估计涉及依赖参数的高维滋扰的估计方程中估计一个低维参数。一个中心示例是因果推理中（局部）分位数处理效应（（L）QTE）的有效估计方程，涉及在分位数以估计的分位数评估的协方差累积分布函数。借记机学习（DML）是一种使用灵活的机器学习方法估算高维滋扰的数据分解方法，但是将其应用于参数依赖性滋扰的问题是不切实际的。对于（L）QTE，DML要求我们学习整个协变量累积分布函数。相反，我们提出了局部偏见的机器学习（LDML），该学习避免了这一繁重的步骤，并且只需要对参数进行一次初始粗糙猜测而估算烦恼。对于（L）QTE，LDML仅涉及学习两个回归功能，这是机器学习方法的标准任务。我们证明，在松弛速率条件下，我们的估计量与使用未知的真实滋扰的不可行的估计器具有相同的有利渐近行为。因此，LDML值得注意的是，当我们必须控制许多协变量和/或灵活的关系时，如（l）QTES在（（l）QTES）中，实际上可以有效地估算重要数量，例如（l）QTES。

As one of the most important psychic stress reactions, micro-expressions (MEs), are spontaneous and transient facial expressions that can reveal the genuine emotions of human beings. Thus, recognizing MEs (MER) automatically is becoming increasingly crucial in the field of affective computing, and provides essential technical support in lie detection, psychological analysis and other areas. However, the lack of abundant ME data seriously restricts the development of cutting-edge data-driven MER models. Despite the recent efforts of several spontaneous ME datasets to alleviate this problem, it is still a tiny amount of work. To solve the problem of ME data hunger, we construct a dynamic spontaneous ME dataset with the largest current ME data scale, called DFME (Dynamic Facial Micro-expressions), which includes 7,526 well-labeled ME videos induced by 671 participants and annotated by more than 20 annotators throughout three years. Afterwards, we adopt four classical spatiotemporal feature learning models on DFME to perform MER experiments to objectively verify the validity of DFME dataset. In addition, we explore different solutions to the class imbalance and key-frame sequence sampling problems in dynamic MER respectively on DFME, so as to provide a valuable reference for future research. The comprehensive experimental results show that our DFME dataset can facilitate the research of automatic MER, and provide a new benchmark for MER. DFME will be published via https://mea-lab-421.github.io.

Cashews are grown by over 3 million smallholders in more than 40 countries worldwide as a principal source of income. As the third largest cashew producer in Africa, Benin has nearly 200,000 smallholder cashew growers contributing 15% of the country's national export earnings. However, a lack of information on where and how cashew trees grow across the country hinders decision-making that could support increased cashew production and poverty alleviation. By leveraging 2.4-m Planet Basemaps and 0.5-m aerial imagery, newly developed deep learning algorithms, and large-scale ground truth datasets, we successfully produced the first national map of cashew in Benin and characterized the expansion of cashew plantations between 2015 and 2021. In particular, we developed a SpatioTemporal Classification with Attention (STCA) model to map the distribution of cashew plantations, which can fully capture texture information from discriminative time steps during a growing season. We further developed a Clustering Augmented Self-supervised Temporal Classification (CASTC) model to distinguish high-density versus low-density cashew plantations by automatic feature extraction and optimized clustering. Results show that the STCA model has an overall accuracy of 80% and the CASTC model achieved an overall accuracy of 77.9%. We found that the cashew area in Benin has doubled from 2015 to 2021 with 60% of new plantation development coming from cropland or fallow land, while encroachment of cashew plantations into protected areas has increased by 70%. Only half of cashew plantations were high-density in 2021, suggesting high potential for intensification. Our study illustrates the power of combining high-resolution remote sensing imagery and state-of-the-art deep learning algorithms to better understand tree crops in the heterogeneous smallholder landscape.

It has been observed in practice that applying pruning-at-initialization methods to neural networks and training the sparsified networks can not only retain the testing performance of the original dense models, but also sometimes even slightly boost the generalization performance. Theoretical understanding for such experimental observations are yet to be developed. This work makes the first attempt to study how different pruning fractions affect the model's gradient descent dynamics and generalization. Specifically, this work considers a classification task for overparameterized two-layer neural networks, where the network is randomly pruned according to different rates at the initialization. It is shown that as long as the pruning fraction is below a certain threshold, gradient descent can drive the training loss toward zero and the network exhibits good generalization performance. More surprisingly, the generalization bound gets better as the pruning fraction gets larger. To complement this positive result, this work further shows a negative result: there exists a large pruning fraction such that while gradient descent is still able to drive the training loss toward zero (by memorizing noise), the generalization performance is no better than random guessing. This further suggests that pruning can change the feature learning process, which leads to the performance drop of the pruned neural network. Up to our knowledge, this is the \textbf{first} generalization result for pruned neural networks, suggesting that pruning can improve the neural network's generalization.

Designing better deep networks and better reinforcement learning (RL) algorithms are both important for deep RL. This work focuses on the former. Previous methods build the network with several modules like CNN, LSTM and Attention. Recent methods combine the Transformer with these modules for better performance. However, it requires tedious optimization skills to train a network composed of mixed modules, making these methods inconvenient to be used in practice. In this paper, we propose to design \emph{pure Transformer-based networks} for deep RL, aiming at providing off-the-shelf backbones for both the online and offline settings. Specifically, the Transformer in Transformer (TIT) backbone is proposed, which cascades two Transformers in a very natural way: the inner one is used to process a single observation, while the outer one is responsible for processing the observation history; combining both is expected to extract spatial-temporal representations for good decision-making. Experiments show that TIT can achieve satisfactory performance in different settings, consistently.

Abstractive dialogue summarization has long been viewed as an important standalone task in natural language processing, but no previous work has explored the possibility of whether abstractive dialogue summarization can also be used as a means to boost an NLP system's performance on other important dialogue comprehension tasks. In this paper, we propose a novel type of dialogue summarization task - STRUctured DiaLoguE Summarization - that can help pre-trained language models to better understand dialogues and improve their performance on important dialogue comprehension tasks. We further collect human annotations of STRUDEL summaries over 400 dialogues and introduce a new STRUDEL dialogue comprehension modeling framework that integrates STRUDEL into a graph-neural-network-based dialogue reasoning module over transformer encoder language models to improve their dialogue comprehension abilities. In our empirical experiments on two important downstream dialogue comprehension tasks - dialogue question answering and dialogue response prediction - we show that our STRUDEL dialogue comprehension model can significantly improve the dialogue comprehension performance of transformer encoder language models.