Stefano Teso

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Andrea PasseriniAssociate Professor, University of TrentoVerified email at disi.unitn.it
Luc De Raedtprofessor, KU Leuven & guestprofessor, Örebro UniversityVerified email at cs.kuleuven.be
Fausto GiunchigliaProfessor of Computer Science, Università di TrentoVerified email at unitn.it
Samuel KolbKU LeuvenVerified email at kuleuven.be
Paolo DragoneTwitterVerified email at twitter.com
Mohit KumarPhD Student, KU LeuvenVerified email at cs.kuleuven.be
Kristian KerstingProfessor of AI & ML, TU Darmstadt, Co-Director hessian.ai, DFKI, Germany, CLAIRE & ELLISVerified email at cs.tu-darmstadt.de
Wolfgang StammerTechnical University DarmstadtVerified email at cs.tu-darmstadt.de
Andrea BontempelliUniversity of TrentoVerified email at unitn.it
Antonio VergariReader (Associate Professor), University of Edinburgh, UKVerified email at ed.ac.uk
Bruno LepriFondazione Bruno KesslerVerified email at fbk.eu
Guy Van den BroeckAssociate Professor and Samueli Fellow, UCLAVerified email at cs.ucla.edu
Anne-Katrin MahleinInstitute of Sugar Beet Research, GöttingenVerified email at ifz-goettingen.de
Xiaoting ShaoTU DarmstadtVerified email at cs.tu-darmstadt.de
Patrick SchramowskiTU DarmstadtVerified email at cs.tu-darmstadt.de
Emanuele MarconatoUniversity of Pisa and University of TrentoVerified email at unitn.it
Paolo MorettinAssistant professor (RTD-A), University of TrentoVerified email at unitn.it
qiang shenPh.D student of computer scienceVerified email at mails.jlu.edu.cn
Roberto SebastianiFull Professor in Computer Science, DISI, University of Trento, ItalyVerified email at unitn.it
Paolo ViappianiCNRS researcher, LAMSADE, Université Paris Dauphine PSLVerified email at dauphine.psl.eu

Stefano Teso

Assistant Professor, University of Trento

Verified email at unitn.it

Machine Learning Explainable AI Interactive Machine Learning Constraint Learning Structured-output Prediction


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Explanatory interactive machine learning S Teso, K Kersting Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 239-245, 2019	191	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	177*	2020
Learning constraints from examples L De Raedt, A Passerini, S Teso Thirty-Second AAAI Conference on Artificial Intelligence, 2018	78	2018
Structured learning modulo theories S Teso, R Sebastiani, A Passerini Artificial Intelligence 244, 166-187, 2017	47	2017
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	44*	2021
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	44	2018
Constructive preference elicitation by setwise max-margin learning S Teso, A Passerini, P Viappiani arXiv preprint arXiv:1604.06020, 2016	35	2016
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	28	2022
Interactive label cleaning with example-based explanations S Teso, A Bontempelli, F Giunchiglia, A Passerini Advances in Neural Information Processing Systems 34, 2021	28	2021
Leveraging explanations in interactive machine learning: An overview S Teso, Ö Alkan, W Stammer, E Daly Frontiers in Artificial Intelligence 6, 2023	25	2023
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	25*	2020
Constructive preference elicitation P Dragone, S Teso, A Passerini Frontiers in Robotics and AI 4, 71, 2018	23	2018
Coactive critiquing: Elicitation of preferences and features S Teso, P Dragone, A Passerini Thirty-First AAAI Conference on Artificial Intelligence, 2017	21	2017
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	20	2021
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	20	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	20	2014
Constructive preference elicitation over hybrid combinatorial spaces P Dragone, S Teso, A Passerini Thirty-Second AAAI Conference on Artificial Intelligence, 2018	19	2018
Ego-centric graphlets for personality and affective states recognition S Teso, J Staiano, B Lepri, A Passerini, F Pianesi 2013 International Conference on Social Computing, 874-877, 2013	19	2013
Glancenets: Interpretable, leak-proof concept-based models E Marconato, A Passerini, S Teso Advances in Neural Information Processing Systems 35, 21212-21227, 2022	17	2022
Concept-level Debugging of Part-Prototype Networks A Bontempelli, S Teso, F Giunchiglia, A Passerini arXiv preprint arXiv:2205.15769, 2022	17	2022

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