Generated texts from large pretrained language models have been shown to exhibit a variety of harmful, human-like biases about various demographics. These findings prompted large efforts aiming to understand and measure such effects, with the goal of providing benchmarks that can guide the development of techniques mitigating these stereotypical associations. However, as recent research has pointed out, the current benchmarks lack a robust experimental setup, consequently hindering the inference of meaningful conclusions from their evaluation metrics. In this paper, we extend these arguments and demonstrate that existing techniques and benchmarks aiming to measure stereotypes tend to be inaccurate and consist of a high degree of experimental noise that severely limits the knowledge we can gain from benchmarking language models based on them. Accordingly, we propose a new framework for robustly measuring and quantifying biases exhibited by generative language models. Finally, we use this framework to investigate GPT-3's occupational gender bias and propose prompting techniques for mitigating these biases without the need for fine-tuning.

Early on during a pandemic, vaccine availability is limited, requiring prioritisation of different population groups. Evaluating vaccine allocation is therefore a crucial element of pandemics response. In the present work, we develop a model to retrospectively evaluate age-dependent counterfactual vaccine allocation strategies against the COVID-19 pandemic. To estimate the effect of allocation on the expected severe-case incidence, we employ a simulation-assisted causal modelling approach which combines a compartmental infection-dynamics simulation, a coarse-grained, data-driven causal model and literature estimates for immunity waning. We compare Israel's implemented vaccine allocation strategy in 2021 to counterfactual strategies such as no prioritisation, prioritisation of younger age groups or a strict risk-ranked approach; we find that Israel's implemented strategy was indeed highly effective. We also study the marginal impact of increasing vaccine uptake for a given age group and find that increasing vaccinations in the elderly is most effective at preventing severe cases, whereas additional vaccinations for middle-aged groups reduce infections most effectively. Due to its modular structure, our model can easily be adapted to study future pandemics. We demonstrate this flexibility by investigating vaccine allocation strategies for a pandemic with characteristics of the Spanish Flu. Our approach thus helps evaluate vaccination strategies under the complex interplay of core epidemic factors, including age-dependent risk profiles, immunity waning, vaccine availability and spreading rates.

Explainability has become a central requirement for the development, deployment, and adoption of machine learning (ML) models and we are yet to understand what explanation methods can and cannot do. Several factors such as data, model prediction, hyperparameters used in training the model, and random initialization can all influence downstream explanations. While previous work empirically hinted that explanations (E) may have little relationship with the prediction (Y), there is a lack of conclusive study to quantify this relationship. Our work borrows tools from causal inference to systematically assay this relationship. More specifically, we measure the relationship between E and Y by measuring the treatment effect when intervening on their causal ancestors (hyperparameters) (inputs to generate saliency-based Es or Ys). We discover that Y's relative direct influence on E follows an odd pattern; the influence is higher in the lowest-performing models than in mid-performing models, and it then decreases in the top-performing models. We believe our work is a promising first step towards providing better guidance for practitioners who can make more informed decisions in utilizing these explanations by knowing what factors are at play and how they relate to their end task.

Causal discovery, the inference of causal relations from data, is a core task of fundamental importance in all scientific domains, and several new machine learning methods for addressing the causal discovery problem have been proposed recently. However, existing machine learning methods for causal discovery typically require that the data used for inference is pooled and available in a centralized location. In many domains of high practical importance, such as in healthcare, data is only available at local data-generating entities (e.g. hospitals in the healthcare context), and cannot be shared across entities due to, among others, privacy and regulatory reasons. In this work, we address the problem of inferring causal structure - in the form of a directed acyclic graph (DAG) - from a distributed data set that contains both observational and interventional data in a privacy-preserving manner by exchanging updates instead of samples. To this end, we introduce a new federated framework, FED-CD, that enables the discovery of global causal structures both when the set of intervened covariates is the same across decentralized entities, and when the set of intervened covariates are potentially disjoint. We perform a comprehensive experimental evaluation on synthetic data that demonstrates that FED-CD enables effective aggregation of decentralized data for causal discovery without direct sample sharing, even when the contributing distributed data sets cover disjoint sets of interventions. Effective methods for causal discovery in distributed data sets could significantly advance scientific discovery and knowledge sharing in important settings, for instance, healthcare, in which sharing of data across local sites is difficult or prohibited.

Geospatial Information Systems are used by researchers and Humanitarian Assistance and Disaster Response (HADR) practitioners to support a wide variety of important applications. However, collaboration between these actors is difficult due to the heterogeneous nature of geospatial data modalities (e.g., multi-spectral images of various resolutions, timeseries, weather data) and diversity of tasks (e.g., regression of human activity indicators or detecting forest fires). In this work, we present a roadmap towards the construction of a general-purpose neural architecture (GPNA) with a geospatial inductive bias, pre-trained on large amounts of unlabelled earth observation data in a self-supervised manner. We envision how such a model may facilitate cooperation between members of the community. We show preliminary results on the first step of the roadmap, where we instantiate an architecture that can process a wide variety of geospatial data modalities and demonstrate that it can achieve competitive performance with domain-specific architectures on tasks relating to the U.N.'s Sustainable Development Goals.

Many problems in causal inference and economics can be formulated in the framework of conditional moment models, which characterize the target function through a collection of conditional moment restrictions. For nonparametric conditional moment models, efficient estimation often relies on preimposed conditions on various measures of ill-posedness of the hypothesis space, which are hard to validate when flexible models are used. In this work, we address this issue by proposing a procedure that automatically learns representations with controlled measures of ill-posedness. Our method approximates a linear representation defined by the spectral decomposition of a conditional expectation operator, which can be used for kernelized estimators and is known to facilitate minimax optimal estimation in certain settings. We show this representation can be efficiently estimated from data, and establish L2 consistency for the resulting estimator. We evaluate the proposed method on proximal causal inference tasks, exhibiting promising performance on high-dimensional, semi-synthetic data.

潜在变量模型（LVM）的无监督学习被广泛用于表示机器学习中的数据。当这样的模型反映了地面真理因素和将它们映射到观察的机制时，有理由期望它们允许在下游任务中进行概括。但是，众所周知，如果不在模型类上施加限制，通常无法实现此类可识别性保证。非线性独立组件分析是如此，其中LVM通过确定性的非线性函数将统计上独立的变量映射到观察。几个伪造解决方案的家庭完全适合数据，但是可以在通用环境中构建与地面真相因素相对应的。但是，最近的工作表明，限制此类模型的功能类别可能会促进可识别性。具体而言，已经提出了在Jacobian矩阵中收集的部分衍生物的函数类，例如正交坐标转换（OCT），它们强加了Jacobian柱的正交性。在目前的工作中，我们证明了这些转换的子类，共形图，是可识别的，并提供了新颖的理论结果，这表明OCT具有防止虚假解决方案家族在通用环境中破坏可识别性的特性。

标准化流是可易处理的密度模型，可以近似复杂的目标分布，例如物理系统的玻尔兹曼分布。但是，当前的训练流量要么具有寻求模式的行为，要么使用昂贵的MCMC模拟事先生成的目标样本，要么使用具有很高差异的随机损失。为了避免这些问题，我们以退火重要性采样（AIS）增强流量，并最大程度地减少覆盖$ \ alpha $ -divergence的质量，并使用$ \ alpha = 2 $，从而最大程度地减少了重要性的重量差异。我们的方法是流动性Bootstrap（Fab），使用AIS在流动较差的目标区域中生成样品，从而促进了新模式的发现。我们以AIS的最小差异分布来定位，以通过重要性抽样来估计$ \ alpha $ -Divergence。我们还使用优先的缓冲区来存储和重复使用AIS样本。这两个功能显着提高了Fab的性能。我们将FAB应用于复杂的多模式目标，并表明我们可以在以前的方法失败的情况下非常准确地近似它们。据我们所知，我们是第一个仅使用非均衡目标密度学习丙氨酸二肽分子的玻璃体分布，而无需通过分子动力学（MD）模拟生成的样品：FAB与通过最大可能性训练更好的效果，而不是通过最大可能性产生的结果。在MD样品上使用100倍的目标评估。在重新获得重要权重的样品后，我们获得了与地面真相几乎相同的二面角的无偏直方图。

我们如何获得世界模型，这些模型在什么以及我们的行动如何影响它方面都在终止代表外界？我们可以通过与世界互动而获得此类模型，并且我们是否可以说明数学逃亡者与他们与脑海中存在的假设现实的关系？随着机器学习不仅朝着包含观察性的代表性，而且介入介入知识的趋势，我们使用代表学习和小组理论的工具研究了这些问题。在假设我们的执行者对世界上作用的假设，我们提出了学习的方法，不仅要学习感官信息的内部表示，而且还以与世界上的行动和过渡相一致的方式来修改我们的感觉表示的行为。我们使用配备有线性作用在其潜在空间上的组表示的自动编码器，该空间对2步重建进行了训练，例如在组表示上执行合适的同构属性。与现有工作相比，我们的方法对组表示的假设更少，并且代理可以从组中采样的转换。我们从理论上激励我们的方法，并从经验上证明它可以学习群体和环境拓扑的正确表示。我们还将其在轨迹预测中的性能与以前的方法进行比较。

深度神经网络在数据流是I.I.D的规范环境中的预测和分类任务上表现良好，标记的数据很丰富，并且类标签平衡。随着分配变化的挑战，包括非平稳或不平衡数据流。解决了这一挑战的一种强大方法是在大量未标记的数据上对大型编码器进行自我监督的预处理，然后进行特定于任务的调整。鉴于一项新任务，更新这些编码器的权重是具有挑战性的，因为需要微调大量权重，因此，他们忘记了有关先前任务的信息。在目前的工作中，我们提出了一个模型体系结构来解决此问题，以一个离散的瓶颈为基础，其中包含成对的单独和可学习的（键，价值）代码。在此设置中，我们遵循编码；通过离散瓶颈处理表示形式；和解码范式，其中输入被馈送到预处理的编码器中，编码器的输出用于选择最近的键，并将相应的值馈送到解码器以求解当前任务。该模型只能在推理过程中获取和重复使用有限数量的这些（密钥，值）对，从而启用本地化和上下文依赖的模型更新。从理论上讲，我们研究了所提出的模型最小化分布的影响的能力，并表明与（键，值）配对的这种离散瓶颈降低了假设类别的复杂性。我们经验验证了提出的方法在各种基准数据集的挑战性分配转移方案下的好处，并表明所提出的模型将共同的脆弱性降低到非i.i.d。与其他各种基线相比，非平稳培训分布。