As machine learning is increasingly used to help make decisions, there is a demand for these decisions to be explainable. Arguably, the most explainable machine learning models use decision rules. This paper focuses on decision sets, a type of model with unordered rules, which explains each prediction with a single rule. In order to be easy for humans to understand, these rules must be concise. Earlier work on generating optimal decision sets first minimizes the number of rules, and then minimizes the number of literals, but the resulting rules can often be very large. Here we consider a better measure, namely the total size of the decision set in terms of literals. So we are not driven to a small set of rules which require a large number of literals. We provide the first approach to determine minimum-size decision sets that achieve minimum empirical risk and then investigate sparse alternatives where we trade accuracy for size. By finding optimal solutions we show we can build decision set classifiers that are almost as accurate as the best heuristic methods, but far more concise, and hence more explainable.
Methods for computing state similarity in Markov Decision Processes
解决大型概率系统的一种流行方法依赖于基于相似性度量的聚集状态。文献中的许多方法都是启发式的。许多最近的方法依赖于基于互模拟或状态之间的行为等效性的概念的度量（Givan et al，2001，2003; Ferns et al，2004）。这种度量的一个组成部分是概率分布之间的Kantorovich度量。然而，虽然该度量使许多令人满意的理论特性成为可能，但在实践中计算成本很高。在本文中，我们使用网络优化和统计抽样技术来克服这个问题。我们以这种方式获得各种距离函数的MDP状态聚合，不同的时间和空间复杂度之间的权衡，以及聚合的质量。我们提供了这些权衡的实证评估。
Neurodiversity-Aware Multimodal Affective Computing for Neurodevelopmental Assessment: From Norm-Referenced Classification to Context-Sensitive Decision Support
Rzeszow University of Technology（热舒夫工业大学）；VIZJA University（VIZJA大学）
On Computing Probabilistic Explanations for Decision Trees
PUC Chile（智利天主教大学）；Millenium Institute for Foundational Research on Data（数据基础研究千年研究所）；CENIA Chile（智利国家人工智能中心）；UAI Chile（智利安德烈斯·贝略大学）；Carnegie Mellon University（卡内基梅隆大学）
Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing System
Academy of Interdisciplinary Studies, The Hong Kong University of Science and Technology（香港科技大学跨学科研究学院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）
Inapproximability of sufficient reasons for decision trees
Centro Nacional de Inteligencia Artificial（国家人工智能中心）；Instituto de Ingeniería Matemática y Computacional, Universidad Católica de Chile（智利天主教大学数学与计算工程学院）；Instituto Milenio Fundamentos de los Datos（千禧年数据基础研究所）
Q-Sat AI: Machine Learning-Based Decision Support for Data Saturation in Qualitative Studies
Bolu Abant Izzet Baysal University（博卢阿班特伊泽特·巴伊萨尔大学）；Azerbaijan State University of Economics (UNEC)（阿塞拜疆国立经济大学（UNEC））；Sakarya University of Applied Science（萨卡里亚应用科学大学）
