01277nas a2200121   4500008004100000245003900041210003800080260000900118520092900127100002001056700002401076856005501100       2023                        eng d00aAUC-based Selective Classification0 aAUCbased Selective Classification  bPMLR3 aSelective classification (or classification with a reject option) pairs a classifier with a selection function to determine whether or not a prediction should be accepted. This framework trades off coverage (probability of accepting a prediction) with predictive performance, typically measured by distributive loss functions. In many application scenarios, such as credit scoring, performance is instead measured by ranking metrics, such as the Area Under the ROC Curve (AUC). We propose a model-agnostic approach to associate a selection function to a given probabilistic binary classifier. The approach is specifically targeted at optimizing the AUC. We provide both theoretical justifications and a novel algorithm, called AUCROSS, to achieve such a goal. Experiments show that our method succeeds in trading-off coverage for AUC, improving over existing selective classification methods targeted at optimizing accuracy.1 aPugnana, Andrea1 aRuggieri, Salvatore  uhttps://proceedings.mlr.press/v206/pugnana23a.html01339nas a2200121   4500008004100000245006100041210005800102260001500160520095300175100002001128700002401148856004501172       2023                        eng d00aA Model-Agnostic Heuristics for Selective Classification0 aModelAgnostic Heuristics for Selective Classification  bAAAI Press3 aSelective classification (also known as classification with reject option) conservatively extends a classifier with a selection function to determine whether or not a prediction should be accepted (i.e., trusted, used, deployed). This is a highly relevant issue in socially sensitive tasks, such as credit scoring. State-of-the-art approaches rely on Deep Neural Networks (DNNs) that train at the same time both the classifier and the selection function. These approaches are model-specific and computationally expensive. We propose a model-agnostic approach, as it can work with any base probabilistic binary classification algorithm, and it can be scalable to large tabular datasets if the base classifier is so. The proposed algorithm, called SCROSS, exploits a cross-fitting strategy and theoretical results for quantile estimation to build the selection function. Experiments on real-world data show that SCROSS improves over existing methods.1 aPugnana, Andrea1 aRuggieri, Salvatore  uhttps://doi.org/10.1609/aaai.v37i8.2613301024nas a2200109   4500008004100000245003900041210003900080260001500119520071400134100002000848856004600868       2023                        eng d00aTopics in Selective Classification0 aTopics in Selective Classification  bAAAI Press3 aIn recent decades, advancements in information technology allowed Artificial Intelligence (AI) systems to predict future outcomes with unprecedented success. This brought the widespread deployment of these methods in many fields, intending to support decision-making. A pressing question is how to make AI systems robust to common challenges in real-life scenarios and trustworthy. In my work, I plan to explore ways to enhance the trustworthiness of AI through the selective classification framework. In this setting, the AI system can refrain from predicting whenever it is not confident enough, allowing it to trade off coverage, i.e. the percentage of instances that receive a prediction, for performance.1 aPugnana, Andrea  uhttps://doi.org/10.1609/aaai.v37i13.2692501528nas a2200157   4500008004100000245006400041210006400105490000700169520099300176100002401169700002001193700002401213700002001237700001601257856009701273       2022                        eng d00aMethods and tools for causal discovery and causal inference0 aMethods and tools for causal discovery and causal inference0 v123 aCausality is a complex concept, which roots its developments across several fields, such as statistics, economics, epidemiology, computer science, and philosophy. In recent years, the study of causal relationships has become a crucial part of the Artificial Intelligence community, as causality can be a key tool for overcoming some limitations of correlation-based Machine Learning systems. Causality research can generally be divided into two main branches, that is, causal discovery and causal inference. The former focuses on obtaining causal knowledge directly from observational data. The latter aims to estimate the impact deriving from a change of a certain variable over an outcome of interest. This article aims at covering several methodologies that have been developed for both tasks. This survey does not only focus on theoretical aspects. But also provides a practical toolkit for interested researchers and practitioners, including software, datasets, and running examples.1 aNogueira, Ana, Rita1 aPugnana, Andrea1 aRuggieri, Salvatore1 aPedreschi, Dino1 aGama, João  uhttps://kdd.isti.cnr.it/publications/methods-and-tools-causal-discovery-and-causal-inference