Making deep neural networks right for the right scientific reasons by interacting with their explanations von Schramowski, Patrick, Stammer, Wolfgang, Teso, Stefano, Brugger, Anna, Herbert, Franziska, Shao, Xiaoting, Luigs, Hans-Georg, Mahlein, Anne-Katrin, Kersting, Kristian

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Leveraging explanations in interactive machine learning: An overview von Teso, Stefano, Alkan, Öznur, Stammer, Wolfgang, Daly, Elizabeth

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Predictive spreadsheet autocompletion with constraints von Kolb, Samuel, Teso, Stefano, Dries, Anton, De Raedt, Luc

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Interpretability Is in the Mind of the Beholder: A Causal Framework for Human-Interpretable Representation Learning von Marconato, Emanuele, Passerini, Andrea, Teso, Stefano

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Combining learning and constraints for genome-wide protein annotation von Teso, Stefano, Masera, Luca, Diligenti, Michelangelo, Passerini, Andrea

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Putting human behavior predictability in context von Zhang, Wanyi, Shen, Qiang, Teso, Stefano, Lepri, Bruno, Passerini, Andrea, Bison, Ivano, Giunchiglia, Fausto

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Constructive Preference Elicitation von Dragone, Paolo, Teso, Stefano, Passerini, Andrea

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Constraint Learning: An Appetizer von Teso, Stefano

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Investigating the association between social interactions and personality states dynamics von Gundogdu, Didem, Finnerty, Ailbhe N., Staiano, Jacopo, Teso, Stefano, Passerini, Andrea, Pianesi, Fabio, Lepri, Bruno

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Structured learning modulo theories von Teso, Stefano, Sebastiani, Roberto, Passerini, Andrea

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Learning MAX-SAT from contextual examples for combinatorial optimisation von Kumar, Mohit, Kolb, Samuel, Teso, Stefano, De Raedt, Luc

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Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation von Kumar, Mohit, Kolb, Samuel, Teso, Stefano, De Raedt, Luc

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Human-in-the-loop handling of knowledge drift von Bontempelli, Andrea, Giunchiglia, Fausto, Passerini, Andrea, Teso, Stefano

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Learning Modulo Theories for constructive preference elicitation von Campigotto, Paolo, Teso, Stefano, Battiti, Roberto, Passerini, Andrea

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Joint probabilistic-logical refinement of multiple protein feature predictors von Teso, Stefano, Passerini, Andrea

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Predictive spreadsheet autocompletion with constraints von Kolb, Samuel, Teso, Stefano, Dries, Anton, De Raedt, Luc

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Combining learning and constraints for genome-wide protein annotation von Teso, Stefano, Masera, Luca, Diligenti, Michelangelo, Passerini, Andrea

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To Transfer or Not to Transfer and Why? Meta-Transfer Learning for Explainable and Controllable Cross-Individual Activity Recognition von Shen, Qiang, Teso, Stefano, Giunchiglia, Fausto, Xu, Hao

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Improved multi-level protein-protein interaction prediction with semantic-based regularization von Saccà, Claudio, Teso, Stefano, Diligenti, Michelangelo, Passerini, Andrea

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Predicting virus mutations through statistical relational learning von Cilia, Elisa, Teso, Stefano, Ammendola, Sergio, Lenaerts, Tom, Passerini, Andrea

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