在可解释的机器学习中，当地的事后解释算法和固有的可解释模型通常被视为竞争方法。在这项工作中，提供了有关Shapley Values的新颖观点，即Shapley Values，这是一种突出的事后解释技术，并表明它与玻璃盒 - 玻璃盒 - gams密切相关，Glassbox-Gam是一种流行的可解释模型。我们介绍了$ n $ -Shapley值，这是Shapley值的自然扩展，该值解释了具有交互条款的个人预测，直到$ n $。随着$ n $的增加，$ n $ shapley的值会收敛于Shapley-Gam，这是原始功能的独特确定分解。从Shapley-GAM中，我们可以计算出任意秩序的Shapley值，从而确切的见解对这些解释的局限性。然后，我们证明Shapley值恢复了订单$ n $的通用添加剂模型，假设我们允许交互条款在解释中订购$ n $。这意味着原始的Shapley值恢复了玻璃盒煤气。在技​​术端，我们表明，选择值函数的不同方式与原始函数的不同功能分解之间存在一对一的对应关系。这为如何选择值函数的问题提供了一个新的观点。我们还对各种标准分类器中存在的可变相互作用程度进行了经验分析，并讨论了我们结果对算法解释的含义。一个用于计算$ n $ shapley值的Python软件包，并在本文中复制结果，请访问\ url {https://github.com/tml-tuebingen/nshap}。

基于梯度的解释算法何时提供有意义的解释？我们提出了一个必要的标准：它们的特征归因需要与数据歧管的切线空间保持一致。为了提供这一假设的证据，我们介绍了一个基于变异自动编码器的框架，该框架允许估计和生成图像歧管。通过跨各种不同数据集的实验 - MNIST，EMNIST，CIFAR10，X射线肺炎和糖尿病性视网膜病变检测 - 我们证明，功能归因与数据的切线相符，结构化和解释性越多倾向于。特别是，由流行的事后方法（例如集成梯度，SmoothGrad和Input $ \ times $梯度）提供的归因往往比原始梯度更与数据歧管更强烈。结果，我们建议解释算法应积极努力将其解释与数据歧管保持一致。在某种程度上，这可以通过对抗训练来实现，从而可以使所有数据集更好地对齐。必须对模型架构或训练算法进行某种形式的调整，因为我们表明单独的神经网络的概括并不意味着模型梯度与数据歧管的一致性。

机器学习通知人类决策者在广泛的任务中的应用。由此产生的问题通常在单个决策者方面配制。我们认为它应该宁愿被描述为一个双人学习问题，其中一个玩家是机器和另一个人。虽然两个玩家都尝试优化最终决定，但设置通常是（1）私人信息的存在和（2）不透明度，即决策者之间的不完美理解。在论文中，我们证明这两个属性都可以复杂化决策。下限量化了最佳地建议的最坏情况的硬度，该决策者是不透明的或可以访问私人信息的决策者。一个上界表明，简单的协调策略几乎最小的最佳。在问题的某些假设下，更高效的学习，例如两个玩家都学会独立采取行动。这种假设是隐含的现有文献中的，例如在机器学习的医学应用中，但理论上尚未被描述或对齐。

Logic Mill is a scalable and openly accessible software system that identifies semantically similar documents within either one domain-specific corpus or multi-domain corpora. It uses advanced Natural Language Processing (NLP) techniques to generate numerical representations of documents. Currently it leverages a large pre-trained language model to generate these document representations. The system focuses on scientific publications and patent documents and contains more than 200 million documents. It is easily accessible via a simple Application Programming Interface (API) or via a web interface. Moreover, it is continuously being updated and can be extended to text corpora from other domains. We see this system as a general-purpose tool for future research applications in the social sciences and other domains.

The analysis of network structure is essential to many scientific areas, ranging from biology to sociology. As the computational task of clustering these networks into partitions, i.e., solving the community detection problem, is generally NP-hard, heuristic solutions are indispensable. The exploration of expedient heuristics has led to the development of particularly promising approaches in the emerging technology of quantum computing. Motivated by the substantial hardware demands for all established quantum community detection approaches, we introduce a novel QUBO based approach that only needs number-of-nodes many qubits and is represented by a QUBO-matrix as sparse as the input graph's adjacency matrix. The substantial improvement on the sparsity of the QUBO-matrix, which is typically very dense in related work, is achieved through the novel concept of separation-nodes. Instead of assigning every node to a community directly, this approach relies on the identification of a separation-node set, which -- upon its removal from the graph -- yields a set of connected components, representing the core components of the communities. Employing a greedy heuristic to assign the nodes from the separation-node sets to the identified community cores, subsequent experimental results yield a proof of concept. This work hence displays a promising approach to NISQ ready quantum community detection, catalyzing the application of quantum computers for the network structure analysis of large scale, real world problem instances.

The following article presents a memetic algorithm with applying deep reinforcement learning (DRL) for solving practically oriented dual resource constrained flexible job shop scheduling problems (DRC-FJSSP). In recent years, there has been extensive research on DRL techniques, but without considering realistic, flexible and human-centered shopfloors. A research gap can be identified in the context of make-to-order oriented discontinuous manufacturing as it is often represented in medium-size companies with high service levels. From practical industry projects in this domain, we recognize requirements to depict flexible machines, human workers and capabilities, setup and processing operations, material arrival times, complex job paths with parallel tasks for bill of material (BOM) manufacturing, sequence-depended setup times and (partially) automated tasks. On the other hand, intensive research has been done on metaheuristics in the context of DRC-FJSSP. However, there is a lack of suitable and generic scheduling methods that can be holistically applied in sociotechnical production and assembly processes. In this paper, we first formulate an extended DRC-FJSSP induced by the practical requirements mentioned. Then we present our proposed hybrid framework with parallel computing for multicriteria optimization. Through numerical experiments with real-world data, we confirm that the framework generates feasible schedules efficiently and reliably. Utilizing DRL instead of random operations leads to better results and outperforms traditional approaches.

The acquisition of high-quality human annotations through crowdsourcing platforms like Amazon Mechanical Turk (MTurk) is more challenging than expected. The annotation quality might be affected by various aspects like annotation instructions, Human Intelligence Task (HIT) design, and wages paid to annotators, etc. To avoid potentially low-quality annotations which could mislead the evaluation of automatic summarization system outputs, we investigate the recruitment of high-quality MTurk workers via a three-step qualification pipeline. We show that we can successfully filter out bad workers before they carry out the evaluations and obtain high-quality annotations while optimizing the use of resources. This paper can serve as basis for the recruitment of qualified annotators in other challenging annotation tasks.

We present NusaCrowd, a collaborative initiative to collect and unite existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have has brought together 137 datasets and 117 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their effectiveness has been demonstrated in multiple experiments. NusaCrowd's data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and its local languages. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and its local languages. Our work is intended to help advance natural language processing research in under-represented languages.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

State-of-the-art language models are often accurate on many question-answering benchmarks with well-defined questions. Yet, in real settings questions are often unanswerable without asking the user for clarifying information. We show that current SotA models often do not ask the user for clarification when presented with imprecise questions and instead provide incorrect answers or "hallucinate". To address this, we introduce CLAM, a framework that first uses the model to detect ambiguous questions, and if an ambiguous question is detected, prompts the model to ask the user for clarification. Furthermore, we show how to construct a scalable and cost-effective automatic evaluation protocol using an oracle language model with privileged information to provide clarifying information. We show that our method achieves a 20.15 percentage point accuracy improvement over SotA on a novel ambiguous question-answering answering data set derived from TriviaQA.