We study a natural extension of classical empirical risk minimization, where the hypothesis space is a random subspace of a given space. In particular, we consider possibly data dependent subspaces spanned by a random subset of the data, recovering as a special case Nystrom approaches for kernel methods. Considering random subspaces naturally leads to computational savings, but the question is whether the corresponding learning accuracy is degraded. These statistical-computational tradeoffs have been recently explored for the least squares loss and self-concordant loss functions, such as the logistic loss. Here, we work to extend these results to convex Lipschitz loss functions, that might not be smooth, such as the hinge loss used in support vector machines. This unified analysis requires developing new proofs, that use different technical tools, such as sub-gaussian inputs, to achieve fast rates. Our main results show the existence of different settings, depending on how hard the learning problem is, for which computational efficiency can be improved with no loss in performance.

在这项工作中，我们考虑线性逆问题$ y = ax + \ epsilon $，其中$ a \ colon x \ to y $是可分离的hilbert spaces $ x $和$ y $之间的已知线性运算符，$ x $。 $ x $和$ \ epsilon $中的随机变量是$ y $的零平均随机过程。该设置涵盖成像中的几个逆问题，包括去噪，去束和X射线层析造影。在古典正规框架内，我们专注于正则化功能的情况下未能先验，而是从数据中学习。我们的第一个结果是关于均方误差的最佳广义Tikhonov规则器的表征。我们发现它完全独立于前向操作员$ a $，并仅取决于$ x $的平均值和协方差。然后，我们考虑从两个不同框架中设置的有限训练中学习常规程序的问题：一个监督，根据$ x $和$ y $的样本，只有一个无人监督，只基于$ x $的样本。在这两种情况下，我们证明了泛化界限，在X $和$ \ epsilon $的分发的一些弱假设下，包括子高斯变量的情况。我们的界限保持在无限尺寸的空间中，从而表明更精细和更细的离散化不会使这个学习问题更加困难。结果通过数值模拟验证。

我们研究了非参数脊的最小二乘的学习属性。特别是，我们考虑常见的估计人的估计案例，由比例依赖性内核定义，并专注于规模的作用。这些估计器内插数据，可以显示规模来通过条件号控制其稳定性。我们的分析表明，这是不同的制度，具体取决于样本大小，其尺寸与问题的平滑度之间的相互作用。实际上，当样本大小小于数据维度中的指数时，可以选择比例，以便学习错误减少。随着样本尺寸变大，总体错误停止减小但有趣地可以选择规模，使得噪声引起的差异仍然存在界线。我们的分析结合了概率，具有来自插值理论的许多分析技术。

Machine-Learned Likelihoods (MLL) is a method that, by combining modern machine-learning classification techniques with likelihood-based inference tests, allows to estimate the experimental sensitivity of high-dimensional data sets. We extend the MLL method by including the exclusion hypothesis tests and show that the addition of Kernel Density Estimators avoids the need to bin the classifier output in order to extract the resulting one-dimensional signal and background probability density functions. We first test our method on toy models generated with multivariate Gaussian distributions, where the true probability distribution functions are known. We then apply it to a case of interest in the search for new physics at the HL-LHC, in which a $Z^\prime$ boson decays into lepton pairs, comparing the performance of our method for estimating 95\% CL exclusion limits to the results obtained applying a binned likelihood to the machine-learning classifier output.

关于在线信息行为的数据的日益增长的可用性为政治传播研究带来了新的可能性。但是，这些数据的数量和多样性使它们难以分析，并提示需要开发自动化内容方法，这些方法依赖于广泛的自然语言处理技术（例如机器学习或基于神经网络）。在本文中，我们讨论如何使用这些技术来检测不同平台的政治内容。使用三个验证数据集，其中包括来自在线平台的各种政治和非政治文本文档，我们系统地比较了依赖词典，监督机器学习或神经网络的三组检测技术的性能。我们还使用大型检测模型的大集合（n = 66）检查了不同数据预处理模式（例如，驱动和停止词）对这些技术的低成本实现的影响。我们的结果表明，预处理对模型性能的影响有限，与基于神经网络和机器学习模型所获得的嘈杂数据的最佳结果相比，基于嘈杂的数据的基于词典模型的更强性能。

Advances in computer vision and machine learning techniques have led to significant development in 2D and 3D human pose estimation from RGB cameras, LiDAR, and radars. However, human pose estimation from images is adversely affected by occlusion and lighting, which are common in many scenarios of interest. Radar and LiDAR technologies, on the other hand, need specialized hardware that is expensive and power-intensive. Furthermore, placing these sensors in non-public areas raises significant privacy concerns. To address these limitations, recent research has explored the use of WiFi antennas (1D sensors) for body segmentation and key-point body detection. This paper further expands on the use of the WiFi signal in combination with deep learning architectures, commonly used in computer vision, to estimate dense human pose correspondence. We developed a deep neural network that maps the phase and amplitude of WiFi signals to UV coordinates within 24 human regions. The results of the study reveal that our model can estimate the dense pose of multiple subjects, with comparable performance to image-based approaches, by utilizing WiFi signals as the only input. This paves the way for low-cost, broadly accessible, and privacy-preserving algorithms for human sensing.

Due to the environmental impacts caused by the construction industry, repurposing existing buildings and making them more energy-efficient has become a high-priority issue. However, a legitimate concern of land developers is associated with the buildings' state of conservation. For that reason, infrared thermography has been used as a powerful tool to characterize these buildings' state of conservation by detecting pathologies, such as cracks and humidity. Thermal cameras detect the radiation emitted by any material and translate it into temperature-color-coded images. Abnormal temperature changes may indicate the presence of pathologies, however, reading thermal images might not be quite simple. This research project aims to combine infrared thermography and machine learning (ML) to help stakeholders determine the viability of reusing existing buildings by identifying their pathologies and defects more efficiently and accurately. In this particular phase of this research project, we've used an image classification machine learning model of Convolutional Neural Networks (DCNN) to differentiate three levels of cracks in one particular building. The model's accuracy was compared between the MSX and thermal images acquired from two distinct thermal cameras and fused images (formed through multisource information) to test the influence of the input data and network on the detection results.

The advances in Artificial Intelligence are creating new opportunities to improve lives of people around the world, from business to healthcare, from lifestyle to education. For example, some systems profile the users using their demographic and behavioral characteristics to make certain domain-specific predictions. Often, such predictions impact the life of the user directly or indirectly (e.g., loan disbursement, determining insurance coverage, shortlisting applications, etc.). As a result, the concerns over such AI-enabled systems are also increasing. To address these concerns, such systems are mandated to be responsible i.e., transparent, fair, and explainable to developers and end-users. In this paper, we present ComplAI, a unique framework to enable, observe, analyze and quantify explainability, robustness, performance, fairness, and model behavior in drift scenarios, and to provide a single Trust Factor that evaluates different supervised Machine Learning models not just from their ability to make correct predictions but from overall responsibility perspective. The framework helps users to (a) connect their models and enable explanations, (b) assess and visualize different aspects of the model, such as robustness, drift susceptibility, and fairness, and (c) compare different models (from different model families or obtained through different hyperparameter settings) from an overall perspective thereby facilitating actionable recourse for improvement of the models. It is model agnostic and works with different supervised machine learning scenarios (i.e., Binary Classification, Multi-class Classification, and Regression) and frameworks. It can be seamlessly integrated with any ML life-cycle framework. Thus, this already deployed framework aims to unify critical aspects of Responsible AI systems for regulating the development process of such real systems.

Model calibration, which is concerned with how frequently the model predicts correctly, not only plays a vital part in statistical model design, but also has substantial practical applications, such as optimal decision-making in the real world. However, it has been discovered that modern deep neural networks are generally poorly calibrated due to the overestimation (or underestimation) of predictive confidence, which is closely related to overfitting. In this paper, we propose Annealing Double-Head, a simple-to-implement but highly effective architecture for calibrating the DNN during training. To be precise, we construct an additional calibration head-a shallow neural network that typically has one latent layer-on top of the last latent layer in the normal model to map the logits to the aligned confidence. Furthermore, a simple Annealing technique that dynamically scales the logits by calibration head in training procedure is developed to improve its performance. Under both the in-distribution and distributional shift circumstances, we exhaustively evaluate our Annealing Double-Head architecture on multiple pairs of contemporary DNN architectures and vision and speech datasets. We demonstrate that our method achieves state-of-the-art model calibration performance without post-processing while simultaneously providing comparable predictive accuracy in comparison to other recently proposed calibration methods on a range of learning tasks.

Dataset scaling, also known as normalization, is an essential preprocessing step in a machine learning pipeline. It is aimed at adjusting attributes scales in a way that they all vary within the same range. This transformation is known to improve the performance of classification models, but there are several scaling techniques to choose from, and this choice is not generally done carefully. In this paper, we execute a broad experiment comparing the impact of 5 scaling techniques on the performances of 20 classification algorithms among monolithic and ensemble models, applying them to 82 publicly available datasets with varying imbalance ratios. Results show that the choice of scaling technique matters for classification performance, and the performance difference between the best and the worst scaling technique is relevant and statistically significant in most cases. They also indicate that choosing an inadequate technique can be more detrimental to classification performance than not scaling the data at all. We also show how the performance variation of an ensemble model, considering different scaling techniques, tends to be dictated by that of its base model. Finally, we discuss the relationship between a model's sensitivity to the choice of scaling technique and its performance and provide insights into its applicability on different model deployment scenarios. Full results and source code for the experiments in this paper are available in a GitHub repository.\footnote{https://github.com/amorimlb/scaling\_matters}