Explanatory interactive machine learning S Teso, K Kersting Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 239-245, 2019 | 241 | 2019 |
Making deep neural networks right for the right scientific reasons by interacting with their explanations P Schramowski, W Stammer, S Teso, A Brugger, F Herbert, X Shao, ... Nature Machine Intelligence 2 (8), 476-486, 2020 | 230 | 2020 |
Learning constraints from examples L De Raedt, A Passerini, S Teso Thirty-Second AAAI Conference on Artificial Intelligence, 2018 | 89 | 2018 |
Semantic probabilistic layers for neuro-symbolic learning K Ahmed, S Teso, KW Chang, G Van den Broeck, A Vergari Advances in Neural Information Processing Systems 35, 29944-29959, 2022 | 61 | 2022 |
A Compositional Atlas of Tractable Circuit Operations: From Simple Transformations to Complex Information-Theoretic Queries A Vergari, YJ Choi, A Liu, S Teso, GV Broeck Advances in Neural Information Processing Systems 34, 2021 | 56* | 2021 |
Structured learning modulo theories S Teso, R Sebastiani, A Passerini Artificial Intelligence 244, 166-187, 2017 | 51 | 2017 |
Learning SMT (LRA) constraints using SMT solvers S Kolb, S Teso, A Passerini, L De Raedt Proceedings of the Twenty-Seventh International Joint Conference on …, 2018 | 50 | 2018 |
Leveraging explanations in interactive machine learning: An overview S Teso, Ö Alkan, W Stammer, E Daly Frontiers in Artificial Intelligence 6, 2023 | 43 | 2023 |
Constructive preference elicitation by setwise max-margin learning S Teso, A Passerini, P Viappiani arXiv preprint arXiv:1604.06020, 2016 | 37 | 2016 |
Glancenets: Interpretable, leak-proof concept-based models E Marconato, A Passerini, S Teso Advances in Neural Information Processing Systems 35, 21212-21227, 2022 | 36 | 2022 |
Interactive label cleaning with example-based explanations S Teso, A Bontempelli, F Giunchiglia, A Passerini Advances in Neural Information Processing Systems 34, 2021 | 35 | 2021 |
Concept-level Debugging of Part-Prototype Networks A Bontempelli, S Teso, F Giunchiglia, A Passerini arXiv preprint arXiv:2205.15769, 2022 | 32 | 2022 |
Efficient Generation of Structured Objects with Constrained Adversarial Networks L Di Liello, P Ardino, J Gobbi, P Morettin, S Teso, A Passerini Advances in Neural Information Processing Systems 33, 2020 | 30* | 2020 |
Putting human behavior predictability in context W Zhang, Q Shen, S Teso, B Lepri, A Passerini, I Bison, F Giunchiglia EPJ Data Science 10 (1), 42, 2021 | 26 | 2021 |
Constructive preference elicitation P Dragone, S Teso, A Passerini Frontiers in Robotics and AI 4, 71, 2018 | 24 | 2018 |
Coactive critiquing: Elicitation of preferences and features S Teso, P Dragone, A Passerini Thirty-First AAAI Conference on Artificial Intelligence, 2017 | 23 | 2017 |
Toward Faithful Explanatory Active Learning with Self-explainable Neural Nets S Teso Proceedings of the Workshop on Interactive Adaptive Learning (IAL 2019), 4-16, 2019 | 22 | 2019 |
Improved multi-level protein–protein interaction prediction with semantic-based regularization C Sacca, S Teso, M Diligenti, A Passerini BMC bioinformatics 15 (1), 103, 2014 | 21 | 2014 |
Constructive preference elicitation over hybrid combinatorial spaces P Dragone, S Teso, A Passerini Thirty-Second AAAI Conference on Artificial Intelligence, 2018 | 20 | 2018 |
Investigating the association between social interactions and personality states dynamics D Gundogdu, AN Finnerty, J Staiano, S Teso, A Passerini, F Pianesi, ... Royal Society open science 4 (9), 170194, 2017 | 20 | 2017 |