The performance of the Deep Learning (DL) models depends on the quality of labels. In some areas, the involvement of human annotators may lead to noise in the data. When these corrupted labels are blindly regarded as the ground truth (GT), DL models suffer from performance deficiency. This paper presents a method that aims to learn a confident model in the presence of noisy labels. This is done in conjunction with estimating the uncertainty of multiple annotators. We robustly estimate the predictions given only the noisy labels by adding entropy or information-based regularizer to the classifier network. We conduct our experiments on a noisy version of MNIST, CIFAR-10, and FMNIST datasets. Our empirical results demonstrate the robustness of our method as it outperforms or performs comparably to other state-of-the-art (SOTA) methods. In addition, we evaluated the proposed method on the curated dataset, where the noise type and level of various annotators depend on the input image style. We show that our approach performs well and is adept at learning annotators' confusion. Moreover, we demonstrate how our model is more confident in predicting GT than other baselines. Finally, we assess our approach for segmentation problem and showcase its effectiveness with experiments.

Federated learning has become a popular machine learning paradigm with many potential real-life applications, including recommendation systems, the Internet of Things (IoT), healthcare, and self-driving cars. Though most current applications focus on classification-based tasks, learning personalized generative models remains largely unexplored, and their benefits in the heterogeneous setting still need to be better understood. This work proposes a novel architecture combining global client-agnostic and local client-specific generative models. We show that using standard techniques for training federated models, our proposed model achieves privacy and personalization that is achieved by implicitly disentangling the globally-consistent representation (i.e. content) from the client-dependent variations (i.e. style). Using such decomposition, personalized models can generate locally unseen labels while preserving the given style of the client and can predict the labels for all clients with high accuracy by training a simple linear classifier on the global content features. Furthermore, disentanglement enables other essential applications, such as data anonymization, by sharing only content. Extensive experimental evaluation corroborates our findings, and we also provide partial theoretical justifications for the proposed approach.

最近，“ SP”（随机Polyak步长）方法已成为一种竞争自适应方法，用于设置SGD的步骤尺寸。SP可以解释为专门针对插值模型的方法，因为它求解了插值方程。SP通过使用模型的局部线性化来求解这些方程。我们进一步迈出一步，并开发一种解决模型局部二阶近似的插值方程的方法。我们最终的方法SP2使用Hessian-Vector产品来加快SP的收敛性。此外，在二阶方法中，SP2的设计绝不依赖于正定的Hessian矩阵或目标函数的凸度。我们显示SP2在矩阵完成，非凸测试问题和逻辑回归方面非常有竞争力。我们还提供了关于Quadratics总和的融合理论。

具有自适应缩放不同功能的方法在解决鞍点问题方面起着关键作用，这主要是由于亚当在解决对抗机器学习问题（包括gans训练）方面的受欢迎程度。本文对解决SPPS的以下缩放技术进行了理论分析：众所周知的Adam和Rmsprop缩放以及基于Hutchison近似的较新的Adahessian和Oasis。我们将额外的梯度及其改进的版本带有负动量作为基本方法。关于gan的实验研究不仅对亚当，而且对其他不太流行的方法显示出良好的适用性。

随机递归梯度算法（SARAH）算法是随机梯度下降（SGD）算法的方差降低的变型，其需要不时地渐变目标函数的梯度。在本文中，我们消除了完全梯度计算的必要性。这是通过使用在每个时代中获得的随机重新洗脱策略和聚集随机梯度来实现的。聚集的随机梯度是SARAH算法中全梯度的估计。我们提供了对所提出的方法的理论分析，并在展示这种方法效率的数值实验中得出本文。

个性化联合学习（PFL）最近看到了巨大的进步，允许设计新颖的机器学习应用来保护培训数据的隐私。该领域的现有理论结果主要关注分布式优化以实现最小化问题。本文是第一个研究马鞍点问题的PFL（涵盖更广泛的优化问题），允许更丰富的应用程序，需要更多地解决最小化问题。在这项工作中，我们考虑最近提出的PFL设置与混合目标函数，一种方法将全球模型与当地分布式学习者相结合的方法。与最先前的工作不同，这仅考虑集中设置，我们在更一般和分散的设置中工作，允许我们设计和分析将设备连接到网络的更实用和联合的方法。我们提出了新的算法来解决这个问题，并在随机和确定性案例中提供平滑（强）凸起（强）凹凸点问题的理论分析。双线性问题的数值实验和对抗噪声的神经网络展示了所提出的方法的有效性。

Landing an unmanned aerial vehicle unmanned aerial vehicle (UAV) on top of an unmanned surface vehicle (USV) in harsh open waters is a challenging problem, owing to forces that can damage the UAV due to a severe roll and/or pitch angle of the USV during touchdown. To tackle this, we propose a novel model predictive control (MPC) approach enabling a UAV to land autonomously on a USV in these harsh conditions. The MPC employs a novel objective function and an online decomposition of the oscillatory motion of the vessel to predict, attempt, and accomplish the landing during near-zero tilt of the landing platform. The nonlinear prediction of the motion of the vessel is performed using visual data from an onboard camera. Therefore, the system does not require any communication with the USV or a control station. The proposed method was analyzed in numerous robotics simulations in harsh and extreme conditions and further validated in various real-world scenarios.

We develop theory and methods that use the graph Laplacian to analyze the geometry of the underlying manifold of point clouds. Our theory provides theoretical guarantees and explicit bounds on the functional form of the graph Laplacian, in the case when it acts on functions defined close to singularities of the underlying manifold. We also propose methods that can be used to estimate these geometric properties of the point cloud, which are based on the theoretical guarantees.

Nearly all jurisdictions in the United States require a professional license exam, commonly referred to as "the Bar Exam," as a precondition for law practice. To even sit for the exam, most jurisdictions require that an applicant completes at least seven years of post-secondary education, including three years at an accredited law school. In addition, most test-takers also undergo weeks to months of further, exam-specific preparation. Despite this significant investment of time and capital, approximately one in five test-takers still score under the rate required to pass the exam on their first try. In the face of a complex task that requires such depth of knowledge, what, then, should we expect of the state of the art in "AI?" In this research, we document our experimental evaluation of the performance of OpenAI's `text-davinci-003` model, often-referred to as GPT-3.5, on the multistate multiple choice (MBE) section of the exam. While we find no benefit in fine-tuning over GPT-3.5's zero-shot performance at the scale of our training data, we do find that hyperparameter optimization and prompt engineering positively impacted GPT-3.5's zero-shot performance. For best prompt and parameters, GPT-3.5 achieves a headline correct rate of 50.3% on a complete NCBE MBE practice exam, significantly in excess of the 25% baseline guessing rate, and performs at a passing rate for both Evidence and Torts. GPT-3.5's ranking of responses is also highly-correlated with correctness; its top two and top three choices are correct 71% and 88% of the time, respectively, indicating very strong non-entailment performance. While our ability to interpret these results is limited by nascent scientific understanding of LLMs and the proprietary nature of GPT, we believe that these results strongly suggest that an LLM will pass the MBE component of the Bar Exam in the near future.

The future of population-based breast cancer screening is likely personalized strategies based on clinically relevant risk models. Mammography-based risk models should remain robust to domain shifts caused by different populations and mammographic devices. Modern risk models do not ensure adaptation across vendor-domains and are often conflated to unintentionally rely on both precursors of cancer and systemic/global mammographic information associated with short- and long-term risk, respectively, which might limit performance. We developed a robust, cross-vendor model for long-term risk assessment. An augmentation-based domain adaption technique, based on flavorization of mammographic views, ensured generalization to an unseen vendor-domain. We trained on samples without diagnosed/potential malignant findings to learn systemic/global breast tissue features, called mammographic texture, indicative of future breast cancer. However, training so may cause erratic convergence. By excluding noise-inducing samples and designing a case-control dataset, a robust ensemble texture model was trained. This model was validated in two independent datasets. In 66,607 Danish women with flavorized Siemens views, the AUC was 0.71 and 0.65 for prediction of interval cancers within two years (ICs) and from two years after screening (LTCs), respectively. In a combination with established risk factors, the model's AUC increased to 0.68 for LTCs. In 25,706 Dutch women with Hologic-processed views, the AUCs were not different from the AUCs in Danish women with flavorized views. The results suggested that the model robustly estimated long-term risk while adapting to an unseen processed vendor-domain. The model identified 8.1% of Danish women accounting for 20.9% of ICs and 14.2% of LTCs.