We study the problem of preserving privacy while still providing high utility in sequential decision making scenarios in a changing environment. We consider abruptly changing environment: the environment remains constant during periods and it changes at unknown time instants. To formulate this problem, we propose a variant of multi-armed bandits called non-stationary stochastic corrupt bandits. We construct an algorithm called SW-KLUCB-CF and prove an upper bound on its utility using the performance measure of regret. The proven regret upper bound for SW-KLUCB-CF is near-optimal in the number of time steps and matches the best known bound for analogous problems in terms of the number of time steps and the number of changes. Moreover, we present a provably optimal mechanism which can guarantee the desired level of local differential privacy while providing high utility.

我们研究了在约束强化学习中有效探索的后验抽样方法。或者，对于现有算法，我们提出了两种简单的算法，这些算法在统计上更有效，更简单地实现和计算便宜。第一种算法基于CMDP的线性公式，第二算法利用CMDP的鞍点公式。我们的经验结果表明，尽管具有简单性，但后取样可实现最先进的表现，在某些情况下，采样明显优于乐观算法。

我们考虑了一个特殊的匪徒问题的情况，即批处理匪徒，其中代理在一定时间段内观察批次的响应。与以前的工作不同，我们考虑了一个更实际相关的以批量学习为中心的情况。也就是说，我们提供了政策不足的遗憾分析，并为候选政策的遗憾展示了上和下限。我们的主要理论结果表明，批处理学习的影响是相对于在线行为的遗憾，批处理大小的多重因素。首先，我们研究了随机线性匪徒的两个设置：有限且无限多手臂的土匪。尽管两种设置的遗憾界限都是相同的，但前者的设置结果在温和的假设下保持。另外，我们为2臂匪徒问题作为重要见解提供了更强大的结果。最后，我们通过进行经验实验并反思最佳批量选择来证明理论结果的一致性。

我们考虑了一个特殊的强盗问题，即批量炸匪。通过推荐制度和电子商务平台的自然限制，我们假设学习代理观察在一定时间内在分组中分批的响应。与以前的工作不同，我们考虑一个更实际相关的批量学习场景。我们为候选政策的遗憾提供了政策 - 不可知的遗憾分析，并展示上下界限。我们的主要理论结果表明，批量学习的影响可以根据在线行为来衡量。最后，我们通过进行经验实验并反映最佳批量尺寸选择来证明理论结果的一致性。

The existing methods for video anomaly detection mostly utilize videos containing identifiable facial and appearance-based features. The use of videos with identifiable faces raises privacy concerns, especially when used in a hospital or community-based setting. Appearance-based features can also be sensitive to pixel-based noise, straining the anomaly detection methods to model the changes in the background and making it difficult to focus on the actions of humans in the foreground. Structural information in the form of skeletons describing the human motion in the videos is privacy-protecting and can overcome some of the problems posed by appearance-based features. In this paper, we present a survey of privacy-protecting deep learning anomaly detection methods using skeletons extracted from videos. We present a novel taxonomy of algorithms based on the various learning approaches. We conclude that skeleton-based approaches for anomaly detection can be a plausible privacy-protecting alternative for video anomaly detection. Lastly, we identify major open research questions and provide guidelines to address them.

People living with dementia often exhibit behavioural and psychological symptoms of dementia that can put their and others' safety at risk. Existing video surveillance systems in long-term care facilities can be used to monitor such behaviours of risk to alert the staff to prevent potential injuries or death in some cases. However, these behaviours of risk events are heterogeneous and infrequent in comparison to normal events. Moreover, analyzing raw videos can also raise privacy concerns. In this paper, we present two novel privacy-protecting video-based anomaly detection approaches to detect behaviours of risks in people with dementia. We either extracted body pose information as skeletons and use semantic segmentation masks to replace multiple humans in the scene with their semantic boundaries. Our work differs from most existing approaches for video anomaly detection that focus on appearance-based features, which can put the privacy of a person at risk and is also susceptible to pixel-based noise, including illumination and viewing direction. We used anonymized videos of normal activities to train customized spatio-temporal convolutional autoencoders and identify behaviours of risk as anomalies. We show our results on a real-world study conducted in a dementia care unit with patients with dementia, containing approximately 21 hours of normal activities data for training and 9 hours of data containing normal and behaviours of risk events for testing. We compared our approaches with the original RGB videos and obtained an equivalent area under the receiver operating characteristic curve performance of 0.807 for the skeleton-based approach and 0.823 for the segmentation mask-based approach. This is one of the first studies to incorporate privacy for the detection of behaviours of risks in people with dementia.

Federated Deep Learning frameworks can be used strategically to monitor Land Use locally and infer environmental impacts globally. Distributed data from across the world would be needed to build a global model for Land Use classification. The need for a Federated approach in this application domain would be to avoid transfer of data from distributed locations and save network bandwidth to reduce communication cost. We use a Federated UNet model for Semantic Segmentation of satellite and street view images. The novelty of the proposed architecture is the integration of Knowledge Distillation to reduce communication cost and response time. The accuracy obtained was above 95% and we also brought in a significant model compression to over 17 times and 62 times for street View and satellite images respectively. Our proposed framework has the potential to be a game-changer in real-time tracking of climate change across the planet.

This paper aims to provide an unsupervised modelling approach that allows for a more flexible representation of text embeddings. It jointly encodes the words and the paragraphs as individual matrices of arbitrary column dimension with unit Frobenius norm. The representation is also linguistically motivated with the introduction of a novel similarity metric. The proposed modelling and the novel similarity metric exploits the matrix structure of embeddings. We then go on to show that the same matrices can be reshaped into vectors of unit norm and transform our problem into an optimization problem over the spherical manifold. We exploit manifold optimization to efficiently train the matrix embeddings. We also quantitatively verify the quality of our text embeddings by showing that they demonstrate improved results in document classification, document clustering, and semantic textual similarity benchmark tests.

We introduce SketchySGD, a stochastic quasi-Newton method that uses sketching to approximate the curvature of the loss function. Quasi-Newton methods are among the most effective algorithms in traditional optimization, where they converge much faster than first-order methods such as SGD. However, for contemporary deep learning, quasi-Newton methods are considered inferior to first-order methods like SGD and Adam owing to higher per-iteration complexity and fragility due to inexact gradients. SketchySGD circumvents these issues by a novel combination of subsampling, randomized low-rank approximation, and dynamic regularization. In the convex case, we show SketchySGD with a fixed stepsize converges to a small ball around the optimum at a faster rate than SGD for ill-conditioned problems. In the non-convex case, SketchySGD converges linearly under two additional assumptions, interpolation and the Polyak-Lojaciewicz condition, the latter of which holds with high probability for wide neural networks. Numerical experiments on image and tabular data demonstrate the improved reliability and speed of SketchySGD for deep learning, compared to standard optimizers such as SGD and Adam and existing quasi-Newton methods.

更多数据有助于我们推广到任务。但是实际数据集可以包含分布（OOD）数据；这可以以异质性的形式出现，例如类内变异性，也可以以时间变化或概念漂移的形式出现。我们在此类问题上展示了一种反直觉现象：任务的概括误差可能是OOD样本数量的非单调函数；少数OOD样品可以改善概括，但是如果OOD样品的数量超出了阈值，则概括误差可能会恶化。我们还表明，如果我们知道哪些样品是OOD，则使用目标和OOD样品之间的加权目标确保概括误差单调减少。我们使用线性分类器在CIFAR-10上的合成数据集和中型神经网络上使用线性分类器演示和分析了此问题。