本文衡量了跨语言寄存器变化的稳定性。寄存器是各种与语言上下文相关的语言。寄存器及其上下文之间的关系是功能的：构成寄存器的语言特征是由交流状况的需求和约束所激发的。该观点假设寄存器应该是通用的，因此我们期望定义寄存器的语言外部环境与寄存器所包含的语言特征集之间存在稳定的关系。在本文中，使用在可比的交流情况下生成的Corpora在60种语言中比较寄存器特定语言中的变化来测试寄存器变化的普遍性和鲁棒性：推文和Wikipedia文章。我们的发现证实了寄存器变化实际上是普遍的预测。

本文分析了基于句法表示的方言分类器在空间和时间上保持稳定的程度。虽然先前的工作表明，语法诱导和地理空间文本分类的结合产生了强大的方言模型，但我们不知道改变语法和人口变化对方言模型的影响是什么。本文为12个英语方言构建了一个测试集，该方言以每月的间隔覆盖三年，在1,120个城市之间进行固定的空间分布。句法表示在基于用法的构造语法范式（CXG）中。随着时间的推移，每个方言的分类性能衰减率使我们能够识别经历句法变化的区域。方言区域内分类精度的分布使我们能够确定方言内部异质性语法的程度。本文的主要贡献是表明，对方言分类模型的严格评估可用于找到空间上的变化和随着时间的变化。

本文使用寄存器预测任务进行了39种语言的基于频率语料库相似性的实验。目的是量化（i）不同语料库与同一语言和（ii）单个语音的同质性之间的距离。这两个目标对于衡量基于语料库的语言分析如何从一个数据集推广到另一个数据集都至关重要。问题在于，以前的工作集中在印欧语上，提出了一个问题，即这些措施是否能够在各种语言上提供强大的概括。本文使用寄存器预测任务来评估跨39种语言的竞争措施：他们能够区分代表不同生产环境的语料库？每个实验都将单个语言的三个语料库与所有语言共享的三个数字寄存器进行比较：社交媒体，网页和Wikipedia。结果表明，语料库相似性的衡量标准保留了不同语言家族，写作系统和形态类型的有效性。此外，当对不域外的语料库，应用于低资源语言以及应用于不同的寄存器集时，这些措施仍然坚固。鉴于我们需要在可用于分析的迅速增加的情况下进行概括，因此这些发现很重要。

本文模拟了17种语言的低资源设置，以评估不同条件下的相似性，稳定性和可靠性。目的是在训练之前使用语料库相似性度量，以预测训练后嵌入的特性。本文的主要贡献是表明可以使用上游语料库相似性度量来预测下游嵌入相似性。然后，通过建模从非常有限的训练数据创建的嵌入式的可靠性，将此发现应用于低资源设置。结果表明，可以使用语料库相似性度量估算低资源嵌入的可靠性，这些度量在少量数据上保持强大。这些发现对评估真正低资源语言的评估具有重大影响，在这种语言中，由于数据限制，这种系统的下游验证方法是不可能的。

本文为太平洋地区的低资源和资源不足语言提供了语言识别模型，重点是以前无法使用的奥地利语语言。准确的语言标识是开发语言资源的重要组成部分。本文采用的方法结合了29种南方语言和171种非澳洲语言，以创建从八个数据源绘制的评估集。在评估了六种语言识别方法之后，我们发现基于跳过嵌入的分类器的性能明显高于替代方法。然后，我们系统地将模型中的非澳洲语言的数量增加到总共800种语言，以评估增加语言库存是否会导致对澳洲感兴趣的澳洲语言的精确预测。该评估发现，增加非澳洲语言库存造成的准确性只有最小的影响。进一步的实验使这些语言识别模型适应了代码转换检测，从而在所有29种语言中都能达到高精度。

Accurate determination of a small molecule candidate (ligand) binding pose in its target protein pocket is important for computer-aided drug discovery. Typical rigid-body docking methods ignore the pocket flexibility of protein, while the more accurate pose generation using molecular dynamics is hindered by slow protein dynamics. We develop a tiered tensor transform (3T) algorithm to rapidly generate diverse protein-ligand complex conformations for both pose and affinity estimation in drug screening, requiring neither machine learning training nor lengthy dynamics computation, while maintaining both coarse-grain-like coordinated protein dynamics and atomistic-level details of the complex pocket. The 3T conformation structures we generate are closer to experimental co-crystal structures than those generated by docking software, and more importantly achieve significantly higher accuracy in active ligand classification than traditional ensemble docking using hundreds of experimental protein conformations. 3T structure transformation is decoupled from the system physics, making future usage in other computational scientific domains possible.

Adversarial imitation learning (AIL) has become a popular alternative to supervised imitation learning that reduces the distribution shift suffered by the latter. However, AIL requires effective exploration during an online reinforcement learning phase. In this work, we show that the standard, naive approach to exploration can manifest as a suboptimal local maximum if a policy learned with AIL sufficiently matches the expert distribution without fully learning the desired task. This can be particularly catastrophic for manipulation tasks, where the difference between an expert and a non-expert state-action pair is often subtle. We present Learning from Guided Play (LfGP), a framework in which we leverage expert demonstrations of multiple exploratory, auxiliary tasks in addition to a main task. The addition of these auxiliary tasks forces the agent to explore states and actions that standard AIL may learn to ignore. Additionally, this particular formulation allows for the reusability of expert data between main tasks. Our experimental results in a challenging multitask robotic manipulation domain indicate that LfGP significantly outperforms both AIL and behaviour cloning, while also being more expert sample efficient than these baselines. To explain this performance gap, we provide further analysis of a toy problem that highlights the coupling between a local maximum and poor exploration, and also visualize the differences between the learned models from AIL and LfGP.

Many problems in machine learning involve bilevel optimization (BLO), including hyperparameter optimization, meta-learning, and dataset distillation. Bilevel problems consist of two nested sub-problems, called the outer and inner problems, respectively. In practice, often at least one of these sub-problems is overparameterized. In this case, there are many ways to choose among optima that achieve equivalent objective values. Inspired by recent studies of the implicit bias induced by optimization algorithms in single-level optimization, we investigate the implicit bias of gradient-based algorithms for bilevel optimization. We delineate two standard BLO methods -- cold-start and warm-start -- and show that the converged solution or long-run behavior depends to a large degree on these and other algorithmic choices, such as the hypergradient approximation. We also show that the inner solutions obtained by warm-start BLO can encode a surprising amount of information about the outer objective, even when the outer parameters are low-dimensional. We believe that implicit bias deserves as central a role in the study of bilevel optimization as it has attained in the study of single-level neural net optimization.

The Covid-19 pandemic induced a vast increase in adolescents diagnosed with eating disorders and hospitalized due to eating disorders. This immense growth stemmed partially from the stress of the pandemic but also from increased exposure to content that promotes eating disorders via social media, which, within the last decade, has become plagued by pro-eating disorder content. This study aimed to create a deep learning model capable of determining whether a given social media post promotes eating disorders based solely on image data. Tweets from hashtags that have been documented to promote eating disorders along with tweets from unrelated hashtags were collected. After prepossessing, these images were labeled as either pro-eating disorder or not based on which Twitter hashtag they were scraped from. Several deep-learning models were trained on the scraped dataset and were evaluated based on their accuracy, F1 score, precision, and recall. Ultimately, the vision transformer model was determined to be the most accurate, attaining an F1 score of 0.877 and an accuracy of 86.7% on the test set. The model, which was applied to unlabeled Twitter image data scraped from "#selfie", uncovered seasonal fluctuations in the relative abundance of pro-eating disorder content, which reached its peak in the summertime. These fluctuations correspond not only to the seasons, but also to stressors, such as the Covid-19 pandemic. Moreover, the Twitter image data indicated that the relative amount of pro-eating disorder content has been steadily rising over the last five years and is likely to continue increasing in the future.

We introduce a pivot for exact selective inference with randomization. Not only does our pivot lead to exact inference in Gaussian regression models, but it is also available in closed form. We reduce the problem of exact selective inference to a bivariate truncated Gaussian distribution. By doing so, we give up some power that is achieved with approximate inference in Panigrahi and Taylor (2022). Yet we always produce narrower confidence intervals than a closely related data-splitting procedure. For popular instances of Gaussian regression, this price -- in terms of power -- in exchange for exact selective inference is demonstrated in simulated experiments and in an HIV drug resistance analysis.