Stefano Teso

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Andrea PasseriniAssociate Professor, University of TrentoEmail verificata su disi.unitn.it
Luc De Raedtprofessor, KU Leuven & guestprofessor, Örebro UniversityEmail verificata su cs.kuleuven.be
Fausto GiunchigliaProfessor of Computer Science, Università di TrentoEmail verificata su unitn.it
Antonio VergariReader (Associate Professor), University of Edinburgh, UKEmail verificata su ed.ac.uk
Samuel KolbKU LeuvenEmail verificata su kuleuven.be
Paolo DragoneTwitterEmail verificata su twitter.com
Kristian KerstingProfessor of AI & ML, TU Darmstadt, Co-Director hessian.ai, DFKI, Germany, CAIRNE & ELLISEmail verificata su cs.tu-darmstadt.de
Mohit KumarPhD Student, KU LeuvenEmail verificata su cs.kuleuven.be
Emanuele MarconatoUniversity of Pisa and University of TrentoEmail verificata su unitn.it
Wolfgang StammerTechnical University DarmstadtEmail verificata su cs.tu-darmstadt.de
Andrea BontempelliUniversity of TrentoEmail verificata su unitn.it
Guy Van den BroeckProfessor and Samueli Fellow, UCLAEmail verificata su cs.ucla.edu
Bruno LepriFondazione Bruno Kessler/Ipazia S.p.a.Email verificata su fbk.eu
Paolo MorettinAssistant professor (RTD-A), University of TrentoEmail verificata su unitn.it
Anne-Katrin MahleinInstitute of Sugar Beet Research, GöttingenEmail verificata su ifz-goettingen.de
Patrick SchramowskiTU DarmstadtEmail verificata su cs.tu-darmstadt.de
qiang shenPh.D student of computer scienceEmail verificata su mails.jlu.edu.cn
YooJung ChoiAssistant Professor, Arizona State UniversityEmail verificata su asu.edu
Anji LiuPh.D. Student of Computer Science, UCLAEmail verificata su cs.ucla.edu
Xiaoting ShaoTU DarmstadtEmail verificata su cs.tu-darmstadt.de

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Stefano Teso

Senior Assistant Professor, University of Trento

Email verificata su unitn.it

Machine Learning Explainable AI Interactive Machine Learning Neuro-Symbolic AI Constraints


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
Explanatory interactive machine learning S Teso, K Kersting Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 239-245, 2019	264	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	262	2020
Learning constraints from examples L De Raedt, A Passerini, S Teso Thirty-Second AAAI Conference on Artificial Intelligence, 2018	99	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	84	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	68*	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	59	2018
Leveraging explanations in interactive machine learning: An overview S Teso, Ö Alkan, W Stammer, E Daly Frontiers in Artificial Intelligence 6, 2023	53	2023
Glancenets: Interpretable, leak-proof concept-based models E Marconato, A Passerini, S Teso Advances in Neural Information Processing Systems 35, 21212-21227, 2022	52	2022
Structured learning modulo theories S Teso, R Sebastiani, A Passerini Artificial Intelligence 244, 166-187, 2017	51	2017
Interactive label cleaning with example-based explanations S Teso, A Bontempelli, F Giunchiglia, A Passerini Advances in Neural Information Processing Systems 34, 2021	42	2021
Concept-level Debugging of Part-Prototype Networks A Bontempelli, S Teso, F Giunchiglia, A Passerini arXiv preprint arXiv:2205.15769, 2022	39	2022
Constructive preference elicitation by setwise max-margin learning S Teso, A Passerini, P Viappiani arXiv preprint arXiv:1604.06020, 2016	39	2016
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	34*	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	31	2021
Constructive preference elicitation P Dragone, S Teso, A Passerini Frontiers in Robotics and AI 4, 71, 2018	25	2018
Federated Multi-Task Attention for Cross-Individual Human Activity Recognition Q Shen, H Feng, R Song, S Teso, F Giunchiglia, H Xu International Joint Conference on Artificial Intelligence, 2022	23	2022
Not all neuro-symbolic concepts are created equal: Analysis and mitigation of reasoning shortcuts E Marconato, S Teso, A Vergari, A Passerini Advances in Neural Information Processing Systems 36, 2024	22	2024
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
Coactive critiquing: Elicitation of preferences and features S Teso, P Dragone, A Passerini Thirty-First AAAI Conference on Artificial Intelligence, 2017	22	2017
Neuro-Symbolic Constraint Programming for Structured Prediction P Dragone, S Teso, A Passerini 15th International Workshop on Neural-Symbolic Learning and Reasoning, 2021	21	2021

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