事实证明，联邦学习（FL）是利用分布式资源的最有希望的范式之一，使一组客户能够协作培训机器学习模型，同时保持数据分散。对该主题兴趣的爆炸性增长导致了几个核心方面的快速发展，例如沟通效率，处理非IID数据，隐私和安全能力。但是，假设客户的培训集被标记，大多数FL仅处理监督任务。为了利用分布式边缘设备上的巨大未标记数据，我们旨在通过解决分散设置中的异常检测问题来扩展FL范式到无监督任务。特别是，我们提出了一种新颖的方法，在这种方法中，通过预处理阶段，客户分组为社区，每个社区都具有相似的多数（即近距离）模式。随后，每个客户社区都以联合方式训练相同的异常检测模型（即自动编码器）。然后共享所得模型并用于检测加入相应联合过程的同一社区客户端内的异常情况。实验表明我们的方法是强大的，它可以检测到与理想分区一致的社区，在这种分区中，知道具有相同近距离模式的客户组。此外，性能要比客户专门培训模型在本地数据上训练，并且与理想社区分区的联合模型相当的性能要好得多。

Deep learning models for learning analytics have become increasingly popular over the last few years; however, these approaches are still not widely adopted in real-world settings, likely due to a lack of trust and transparency. In this paper, we tackle this issue by implementing explainable AI methods for black-box neural networks. This work focuses on the context of online and blended learning and the use case of student success prediction models. We use a pairwise study design, enabling us to investigate controlled differences between pairs of courses. Our analyses cover five course pairs that differ in one educationally relevant aspect and two popular instance-based explainable AI methods (LIME and SHAP). We quantitatively compare the distances between the explanations across courses and methods. We then validate the explanations of LIME and SHAP with 26 semi-structured interviews of university-level educators regarding which features they believe contribute most to student success, which explanations they trust most, and how they could transform these insights into actionable course design decisions. Our results show that quantitatively, explainers significantly disagree with each other about what is important, and qualitatively, experts themselves do not agree on which explanations are most trustworthy. All code, extended results, and the interview protocol are provided at https://github.com/epfl-ml4ed/trusting-explainers.

Student success models might be prone to develop weak spots, i.e., examples hard to accurately classify due to insufficient representation during model creation. This weakness is one of the main factors undermining users' trust, since model predictions could for instance lead an instructor to not intervene on a student in need. In this paper, we unveil the need of detecting and characterizing unknown unknowns in student success prediction in order to better understand when models may fail. Unknown unknowns include the students for which the model is highly confident in its predictions, but is actually wrong. Therefore, we cannot solely rely on the model's confidence when evaluating the predictions quality. We first introduce a framework for the identification and characterization of unknown unknowns. We then assess its informativeness on log data collected from flipped courses and online courses using quantitative analyses and interviews with instructors. Our results show that unknown unknowns are a critical issue in this domain and that our framework can be applied to support their detection. The source code is available at https://github.com/epfl-ml4ed/unknown-unknowns.

One of today's goals for industrial robot systems is to allow fast and easy provisioning for new tasks. Skill-based systems that use planning and knowledge representation have long been one possible answer to this. However, especially with contact-rich robot tasks that need careful parameter settings, such reasoning techniques can fall short if the required knowledge not adequately modeled. We show an approach that provides a combination of task-level planning and reasoning with targeted learning of skill parameters for a task at hand. Starting from a task goal formulated in PDDL, the learnable parameters in the plan are identified and an operator can choose reward functions and parameters for the learning process. A tight integration with a knowledge framework allows to form a prior for learning and the usage of multi-objective Bayesian optimization eases to balance aspects such as safety and task performance that can often affect each other. We demonstrate the efficacy and versatility of our approach by learning skill parameters for two different contact-rich tasks and show their successful execution on a real 7-DOF KUKA-iiwa.

Time series is the most prevalent form of input data for educational prediction tasks. The vast majority of research using time series data focuses on hand-crafted features, designed by experts for predictive performance and interpretability. However, extracting these features is labor-intensive for humans and computers. In this paper, we propose an approach that utilizes irregular multivariate time series modeling with graph neural networks to achieve comparable or better accuracy with raw time series clickstreams in comparison to hand-crafted features. Furthermore, we extend concept activation vectors for interpretability in raw time series models. We analyze these advances in the education domain, addressing the task of early student performance prediction for downstream targeted interventions and instructional support. Our experimental analysis on 23 MOOCs with millions of combined interactions over six behavioral dimensions show that models designed with our approach can (i) beat state-of-the-art educational time series baselines with no feature extraction and (ii) provide interpretable insights for personalized interventions. Source code: https://github.com/epfl-ml4ed/ripple/.

Just like in humans vision plays a fundamental role in guiding adaptive locomotion, when designing the control strategy for a walking assistive technology, Computer Vision may bring substantial improvements when performing an environment-based assistance modulation. In this work, we developed a hip exosuit controller able to distinguish among three different walking terrains through the use of an RGB camera and to adapt the assistance accordingly. The system was tested with seven healthy participants walking throughout an overground path comprising of staircases and level ground. Subjects performed the task with the exosuit disabled (Exo Off), constant assistance profile (Vision Off ), and with assistance modulation (Vision On). Our results showed that the controller was able to promptly classify in real-time the path in front of the user with an overall accuracy per class above the 85%, and to perform assistance modulation accordingly. Evaluation related to the effects on the user showed that Vision On was able to outperform the other two conditions: we obtained significantly higher metabolic savings than Exo Off, with a peak of about -20% when climbing up the staircase and about -16% in the overall path, and than Vision Off when ascending or descending stairs. Such advancements in the field may yield to a step forward for the exploitation of lightweight walking assistive technologies in real-life scenarios.

网络物理系统（CPSS）通常是复杂且至关重要的；因此，确保系统的要求，即规格，很难满足。基于仿真的CPS伪造是一种实用的测试方法，可用于通过仅要求模拟正在测试的系统来提高对系统正确性的信心。由于每个仿真通常在计算上进行密集，因此一个重要的步骤是减少伪造规范所需的仿真数量。我们研究贝叶斯优化（BO），一种样本效率的方法，它学习了一个替代模型，该模型描述了可能的输入信号的参数化与规范评估之间的关系。在本文中，我们改善了使用BO的伪造；首先采用两种突出的BO方法，一种适合本地替代模型，另一个适合当地的替代模型，利用了用户的先验知识。其次，本文介绍了伪造功能的采集函数的表述。基准评估显示，使用BO的局部替代模型来伪造以前难以伪造的基准示例的显着改善。在伪造过程中使用先验知识被证明是在模拟预算有限时特别重要的。对于某些基准问题，采集功能的选择清楚地影响了成功伪造所需的模拟数量。

为了执行机器人操纵任务，核心问题是确定满足任务要求的合适轨迹。存在各种计算此类轨迹的方法，是学习和优化主要驾驶技术。我们的作品建立在从示范中学习（LFD）范式的基础上，专家展示了动作，机器人学会了模仿它们。但是，专家演示不足以捕获各种任务规格，例如掌握对象的时间。在本文中，我们提出了一种新方法，以考虑LFD技能中的正式任务规格。确切地说，我们利用了系统的时间属性的一种表达形式信号时间逻辑（STL），以制定任务规格并使用黑盒优化（BBO）来相应地调整LFD技能。我们使用多个任务展示了我们的方法如何使用STL和BBO来解决LFD限制。

水果和蔬菜的检测，分割和跟踪是精确农业的三个基本任务，实现了机器人的收获和产量估计。但是，现代算法是饥饿的数据，并非总是有可能收集足够的数据来运用最佳性能的监督方法。由于数据收集是一项昂贵且繁琐的任务，因此在农业中使用计算机视觉的能力通常是小企业无法实现的。在此背景下的先前工作之后，我们提出了一种初始弱监督的解决方案，以减少在精确农业应用程序中获得最新检测和细分所需的数据，在这里，我们在这里改进该系统并探索跟踪果实的问题果园。我们介绍了拉齐奥南部（意大利）葡萄的葡萄园案例，因为葡萄由于遮挡，颜色和一般照明条件而难以分割。当有一些可以用作源数据的初始标记数据（例如，葡萄酒葡萄数据）时，我们会考虑这种情况，但与目标数据有很大不同（例如表格葡萄数据）。为了改善目标数据的检测和分割，我们建议使用弱边界框标签训练分割算法，而对于跟踪，我们从运动算法中利用3D结构来生成来自已标记样品的新标签。最后，将两个系统组合成完整的半监督方法。与SOTA监督解决方案的比较表明，我们的方法如何能够训练以很少的标记图像和非常简单的标签来实现高性能的新型号。

在本文中，我们提出了一个新颖的解释性框架，旨在更好地理解面部识别模型作为基本数据特征的表现（受保护的属性：性别，种族，年龄；非保护属性：面部毛发，化妆品，配件，脸部，面部，面部，面部，面部，面部，它们被测试的变化的方向和阻塞，图像失真，情绪）。通过我们的框架，我们评估了十种最先进的面部识别模型，并在两个数据集上的安全性和可用性方面进行了比较，涉及基于性别和种族的六个小组。然后，我们分析图像特征对模型性能的影响。我们的结果表明，当考虑多归因组时，单属分析中出现的趋势消失或逆转，并且性能差异也与非保护属性有关。源代码：https：//cutt.ly/2xwrlia。