隐私保护数据分析研究了在隐私约束下的统计方法。这是现代统计数据中的一个不断提高的挑战，因为机密性保证的实现通常是通过数据扰动而发生的，这可能会决定数据的统计实用性损失。在本文中，我们考虑对频率表中的拟合优点进行隐私测试，这可以说是释放数据的最常见形式，并对私人可能性比率（LR）的大样本行为进行了严格的分析（LR）测试。在$（\ varepsilon，\ delta）$ - 差异隐私的框架下，我们的主要贡献是私人LR测试的功率分析，该测试的特征是通过差异隐私参数测量的机密性之间的权衡取舍（$）（ \ varepsilon，\ delta）$和统计实用程序，通过测试功率测量。这是通过bahadur-rao大偏差扩展获得的，用于私人LR测试的功率，从样本量，表和$（\ varepsilon，\ delta）$，这决定了测试功能的损失。然后，将这样的结果应用于与参数$（\ varepsilon，\ delta）$相关的样本量和表尺寸的影响，对私人LR测试的功率损失。特别是，我们确定$（样本）成本（\ varepsilon，\ delta）$ - 私人LR测试中的差异隐私，即在没有缺少多项式LR测试的功率所需的附加样本量扰动。我们的功率分析依赖于LR的非标准大偏差分析，以及用于I.I.D的新颖（尖锐）大偏差原理的发展。随机矢量，具有独立感兴趣。

计数示意图（CMS）是一个时间和内存有效的随机数据结构，可根据随机哈希的数据提供令牌数据流（即点查询）中代币频率的估计。 CAI，Mitzenmacher和Adams（\ textit {neurips} 2018）提出了CMS的学习增强版本，称为CMS-DP，它依赖于贝叶斯非参与式（BNP）模型通过dirichlet过程（DP），给定数据，估计点查询作为位置查询后验分布的合适平均功能的估计值给定数据。尽管CMS-DP已被证明可以改善CMS的某些方面，但它具有``建设性的''证明的主要缺点，该证明是基于针对DP先验的论点构建的，即对其他非参数priors不使用的论点。在本文中，我们提出了CMS-DP的``贝叶斯''证明，其主要优点是基于原则上可用的参数，在广泛的非参数先验中，这是由归一化的完全随机措施引起的。该结果导致在Power-Law数据流下开发了一种新颖的学习增强的CMS，称为CMS-PYP，该CMS-PYP依赖于Pitman-Yor流程（PYP）的BNP模型。在这个更一般的框架下，我们应用了CMS-DP的``贝叶斯人''证明的论点，适当地适合PYP先验，以计算鉴于Hashed Data。数据和真实文本数据显示，CMS-PYP在估计低频代币方面优于CMS和CMS-DP，这在文本数据中是至关重要的，并且相对于CMS的变化，它具有竞争力还讨论了为低频代币设计的。还讨论了我们BNP方法扩展到更通用的查询。

Self-supervised learning is a popular and powerful method for utilizing large amounts of unlabeled data, for which a wide variety of training objectives have been proposed in the literature. In this study, we perform a Bayesian analysis of state-of-the-art self-supervised learning objectives and propose a unified formulation based on likelihood learning. Our analysis suggests a simple method for integrating self-supervised learning with generative models, allowing for the joint training of these two seemingly distinct approaches. We refer to this combined framework as GEDI, which stands for GEnerative and DIscriminative training. Additionally, we demonstrate an instantiation of the GEDI framework by integrating an energy-based model with a cluster-based self-supervised learning model. Through experiments on synthetic and real-world data, including SVHN, CIFAR10, and CIFAR100, we show that GEDI outperforms existing self-supervised learning strategies in terms of clustering performance by a wide margin. We also demonstrate that GEDI can be integrated into a neural-symbolic framework to address tasks in the small data regime, where it can use logical constraints to further improve clustering and classification performance.

We propose a novel approach for deep learning-based Multi-View Stereo (MVS). For each pixel in the reference image, our method leverages a deep architecture to search for the corresponding point in the source image directly along the corresponding epipolar line. We denote our method DELS-MVS: Deep Epipolar Line Search Multi-View Stereo. Previous works in deep MVS select a range of interest within the depth space, discretize it, and sample the epipolar line according to the resulting depth values: this can result in an uneven scanning of the epipolar line, hence of the image space. Instead, our method works directly on the epipolar line: this guarantees an even scanning of the image space and avoids both the need to select a depth range of interest, which is often not known a priori and can vary dramatically from scene to scene, and the need for a suitable discretization of the depth space. In fact, our search is iterative, which avoids the building of a cost volume, costly both to store and to process. Finally, our method performs a robust geometry-aware fusion of the estimated depth maps, leveraging a confidence predicted alongside each depth. We test DELS-MVS on the ETH3D, Tanks and Temples and DTU benchmarks and achieve competitive results with respect to state-of-the-art approaches.

Timely and effective feedback within surgical training plays a critical role in developing the skills required to perform safe and efficient surgery. Feedback from expert surgeons, while especially valuable in this regard, is challenging to acquire due to their typically busy schedules, and may be subject to biases. Formal assessment procedures like OSATS and GEARS attempt to provide objective measures of skill, but remain time-consuming. With advances in machine learning there is an opportunity for fast and objective automated feedback on technical skills. The SimSurgSkill 2021 challenge (hosted as a sub-challenge of EndoVis at MICCAI 2021) aimed to promote and foster work in this endeavor. Using virtual reality (VR) surgical tasks, competitors were tasked with localizing instruments and predicting surgical skill. Here we summarize the winning approaches and how they performed. Using this publicly available dataset and results as a springboard, future work may enable more efficient training of surgeons with advances in surgical data science. The dataset can be accessed from https://console.cloud.google.com/storage/browser/isi-simsurgskill-2021.

A tractogram is a virtual representation of the brain white matter. It is composed of millions of virtual fibers, encoded as 3D polylines, which approximate the white matter axonal pathways. To date, tractograms are the most accurate white matter representation and thus are used for tasks like presurgical planning and investigations of neuroplasticity, brain disorders, or brain networks. However, it is a well-known issue that a large portion of tractogram fibers is not anatomically plausible and can be considered artifacts of the tracking procedure. With Verifyber, we tackle the problem of filtering out such non-plausible fibers using a novel fully-supervised learning approach. Differently from other approaches based on signal reconstruction and/or brain topology regularization, we guide our method with the existing anatomical knowledge of the white matter. Using tractograms annotated according to anatomical principles, we train our model, Verifyber, to classify fibers as either anatomically plausible or non-plausible. The proposed Verifyber model is an original Geometric Deep Learning method that can deal with variable size fibers, while being invariant to fiber orientation. Our model considers each fiber as a graph of points, and by learning features of the edges between consecutive points via the proposed sequence Edge Convolution, it can capture the underlying anatomical properties. The output filtering results highly accurate and robust across an extensive set of experiments, and fast; with a 12GB GPU, filtering a tractogram of 1M fibers requires less than a minute. Verifyber implementation and trained models are available at https://github.com/FBK-NILab/verifyber.

Semi-supervised learning methods can train high-accuracy machine learning models with a fraction of the labeled training samples required for traditional supervised learning. Such methods do not typically involve close review of the unlabeled training samples, making them tempting targets for data poisoning attacks. In this paper we investigate the vulnerabilities of semi-supervised learning methods to backdoor data poisoning attacks on the unlabeled samples. We show that simple poisoning attacks that influence the distribution of the poisoned samples' predicted labels are highly effective - achieving an average attack success rate as high as 96.9%. We introduce a generalized attack framework targeting semi-supervised learning methods to better understand and exploit their limitations and to motivate future defense strategies.

Extreme wildfires continue to be a significant cause of human death and biodiversity destruction within countries that encompass the Mediterranean Basin. Recent worrying trends in wildfire activity (i.e., occurrence and spread) suggest that wildfires are likely to be highly impacted by climate change. In order to facilitate appropriate risk mitigation, it is imperative to identify the main drivers of extreme wildfires and assess their spatio-temporal trends, with a view to understanding the impacts of global warming on fire activity. To this end, we analyse the monthly burnt area due to wildfires over a region encompassing most of Europe and the Mediterranean Basin from 2001 to 2020, and identify high fire activity during this period in eastern Europe, Algeria, Italy and Portugal. We build an extreme quantile regression model with a high-dimensional predictor set describing meteorological conditions, land cover usage, and orography, for the domain. To model the complex relationships between the predictor variables and wildfires, we make use of a hybrid statistical deep-learning framework that allows us to disentangle the effects of vapour-pressure deficit (VPD), air temperature, and drought on wildfire activity. Our results highlight that whilst VPD, air temperature, and drought significantly affect wildfire occurrence, only VPD affects extreme wildfire spread. Furthermore, to gain insights into the effect of climate change on wildfire activity in the near future, we perturb VPD and temperature according to their observed trends and find evidence that global warming may lead to spatially non-uniform changes in wildfire activity.

Recent video+language datasets cover domains where the interaction is highly structured, such as instructional videos, or where the interaction is scripted, such as TV shows. Both of these properties can lead to spurious cues to be exploited by models rather than learning to ground language. In this paper, we present GrOunded footbAlL commentaries (GOAL), a novel dataset of football (or `soccer') highlights videos with transcribed live commentaries in English. As the course of a game is unpredictable, so are commentaries, which makes them a unique resource to investigate dynamic language grounding. We also provide state-of-the-art baselines for the following tasks: frame reordering, moment retrieval, live commentary retrieval and play-by-play live commentary generation. Results show that SOTA models perform reasonably well in most tasks. We discuss the implications of these results and suggest new tasks for which GOAL can be used. Our codebase is available at: https://gitlab.com/grounded-sport-convai/goal-baselines.

Large language models (LLMs) have been reported to have strong performance on natural language processing tasks. However, performance metrics such as accuracy do not measure the quality of the model in terms of its ability to robustly represent complex linguistic structure. In this work, we propose a framework to evaluate the robustness of linguistic representations using probing tasks. We leverage recent advances in extracting emergent linguistic constructs from LLMs and apply syntax-preserving perturbations to test the stability of these constructs in order to better understand the representations learned by LLMs. Empirically, we study the performance of four LLMs across six different corpora on the proposed robustness measures. We provide evidence that context-free representation (e.g., GloVe) are in some cases competitive with context-dependent representations from modern LLMs (e.g., BERT), yet equally brittle to syntax-preserving manipulations. Emergent syntactic representations in neural networks are brittle, thus our work poses the attention on the risk of comparing such structures to those that are object of a long lasting debate in linguistics.