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ORCIDStefano Teso
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- affiliation: University of Trento, Italy
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Books and Theses
- 2013
[b1]Stefano Teso:
Statistical Relational Learning for Proteomics: Function, Interactions and Evolution. University of Trento, Italy, 2013
Journal Articles
- 2024
- [j16]Giovanni De Toni, Paolo Viappiani, Stefano Teso, Bruno Lepri, Andrea Passerini:
Personalized Algorithmic Recourse with Preference Elicitation. Trans. Mach. Learn. Res. 2024 (2024) - 2023
- [j15]Mohit Kumar
, Samuel Kolb, Stefano Teso, Luc De Raedt:
Learning MAX-SAT from contextual examples for combinatorial optimisation. Artif. Intell. 314: 103794 (2023) - [j14]Emanuele Marconato, Andrea Passerini, Stefano Teso:
Interpretability Is in the Mind of the Beholder: A Causal Framework for Human-Interpretable Representation Learning. Entropy 25(12): 1574 (2023) - [j13]Stefano Teso, Öznur Alkan, Wolfgang Stammer, Elizabeth Daly:
Leveraging explanations in interactive machine learning: An overview. Frontiers Artif. Intell. 6 (2023) - 2022
- [j12]Andrea Bontempelli, Fausto Giunchiglia, Andrea Passerini, Stefano Teso:
Human-in-the-loop handling of knowledge drift. Data Min. Knowl. Discov. 36(5): 1865-1884 (2022) - 2021
- [j11]Paolo Campigotto, Stefano Teso, Roberto Battiti, Andrea Passerini:
Learning Modulo Theories for constructive preference elicitation. Artif. Intell. 295: 103454 (2021) - [j10]Wanyi Zhang, Qiang Shen, Stefano Teso, Bruno Lepri, Andrea Passerini, Ivano Bison, Fausto Giunchiglia:
Putting human behavior predictability in context. EPJ Data Sci. 10(1): 42 (2021) - 2020
- [j9]Stefano Teso, Oliver Hinz:
Challenges in Interactive Machine Learning. Künstliche Intell. 34(2): 127-130 (2020) - [j8]Samuel Kolb, Stefano Teso, Anton Dries, Luc De Raedt:
Predictive spreadsheet autocompletion with constraints. Mach. Learn. 109(2): 307-325 (2020) - [j7]Patrick Schramowski, Wolfgang Stammer, Stefano Teso, Anna Brugger, Franziska Herbert, Xiaoting Shao, Hans-Georg Luigs, Anne-Katrin Mahlein, Kristian Kersting:
Making deep neural networks right for the right scientific reasons by interacting with their explanations. Nat. Mach. Intell. 2(8): 476-486 (2020) - 2019
- [j6]Stefano Teso, Luca Masera, Michelangelo Diligenti, Andrea Passerini:
Combining learning and constraints for genome-wide protein annotation. BMC Bioinform. 20(1): 338:1-338:14 (2019) - 2017
- [j5]Stefano Teso, Roberto Sebastiani, Andrea Passerini:
Structured learning modulo theories. Artif. Intell. 244: 166-187 (2017) - [j4]Paolo Dragone, Stefano Teso, Andrea Passerini:
Constructive Preference Elicitation. Frontiers Robotics AI 4: 71 (2017) - 2014
- [j3]Stefano Teso, Andrea Passerini:
Joint probabilistic-logical refinement of multiple protein feature predictors. BMC Bioinform. 15: 16 (2014) - [j2]Claudio Saccà, Stefano Teso, Michelangelo Diligenti, Andrea Passerini:
Improved multi-level protein¿protein interaction prediction with semantic-based regularization. BMC Bioinform. 15: 103 (2014) - [j1]Elisa Cilia, Stefano Teso, Sergio Ammendola, Tom Lenaerts, Andrea Passerini:
Predicting virus mutations through statistical relational learning. BMC Bioinform. 15: 309 (2014)
Conference and Workshop Papers
- 2023
- [c41]Andrea Bontempelli, Stefano Teso, Katya Tentori, Fausto Giunchiglia, Andrea Passerini:
Concept-level Debugging of Part-Prototype Networks. ICLR 2023 - [c40]Emanuele Marconato, Gianpaolo Bontempo, Elisa Ficarra, Simone Calderara, Andrea Passerini, Stefano Teso:
Neuro-Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal. ICML 2023: 23915-23936 - [c39]Emanuele Marconato, Stefano Teso, Andrea Passerini:
Neuro-Symbolic Reasoning Shortcuts: Mitigation Strategies and their Limitations. NeSy 2023: 162-166 - [c38]Emanuele Marconato, Andrea Passerini, Stefano Teso:
GlanceNets: Interpretable, Leak-proof Concept-based Models. NeSy 2023: 410 - [c37]Kareem Ahmed, Stefano Teso, Kai-Wei Chang, Guy Van den Broeck, Antonio Vergari:
Semantic Probabilistic Layers for Neuro-Symbolic Learning. NeSy 2023: 413 - [c36]Emanuele Marconato, Stefano Teso, Antonio Vergari, Andrea Passerini:
Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts. NeurIPS 2023 - 2022
- [c35]Ivan Donadello, Anthony Hunter, Stefano Teso, Mauro Dragoni:
Machine Learning for Utility Prediction in Argument-Based Computational Persuasion. AAAI 2022: 5592-5599 - [c34]Emanuele Marconato, Gianpaolo Bontempo, Stefano Teso, Elisa Ficarra, Simone Calderara, Andrea Passerini:
Catastrophic Forgetting in Continual Concept Bottleneck Models. ICIAP Workshops 2022: 539-547 - [c33]Qiang Shen, Haotian Feng, Rui Song, Stefano Teso, Fausto Giunchiglia, Hao Xu:
Federated Multi-Task Attention for Cross-Individual Human Activity Recognition. IJCAI 2022: 3423-3429 - [c32]Kareem Ahmed, Stefano Teso, Kai-Wei Chang, Guy Van den Broeck, Antonio Vergari:
Semantic Probabilistic Layers for Neuro-Symbolic Learning. NeurIPS 2022 - [c31]Emanuele Marconato, Andrea Passerini, Stefano Teso:
GlanceNets: Interpretable, Leak-proof Concept-based Models. NeurIPS 2022 - 2021
- [c30]Paolo Morettin, Andrea Passerini, Stefano Teso:
Co-creating Platformer Levels with Constrained Adversarial Networks. IUI Workshops 2021 - [c29]Paolo Dragone, Stefano Teso, Andrea Passerini:
Neuro-Symbolic Constraint Programming for Structured Prediction. NeSy 2021: 6-14 - [c28]Stefano Teso, Andrea Bontempelli, Fausto Giunchiglia, Andrea Passerini:
Interactive Label Cleaning with Example-based Explanations. NeurIPS 2021: 12966-12977 - [c27]Antonio Vergari, YooJung Choi, Anji Liu, Stefano Teso, Guy Van den Broeck:
A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference. NeurIPS 2021: 13189-13201 - 2020
- [c26]Mohit Kumar, Samuel Kolb, Stefano Teso, Luc De Raedt:
Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation. AAAI 2020: 4493-4500 - [c25]Paolo Morettin, Samuel Kolb, Stefano Teso, Andrea Passerini:
Learning Weighted Model Integration Distributions. AAAI 2020: 5224-5231 - [c24]Qiang Shen, Stefano Teso, Wanyi Zhang, Hao Xu, Fausto Giunchiglia:
Multi-Modal Subjective Context Modelling and Recognition. MRC@ECAI 2020: 32-36 - [c23]Andrea Bontempelli, Stefano Teso, Fausto Giunchiglia, Andrea Passerini:
Learning in the Wild with Incremental Skeptical Gaussian Processes. IJCAI 2020: 2886-2892 - [c22]Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Paolo Morettin, Stefano Teso, Andrea Passerini:
Efficient Generation of Structured Objects with Constrained Adversarial Networks. NeurIPS 2020 - [c21]Clément Gautrais, Yann Dauxais, Samuel Kolb, Arcchit Jain, Mohit Kumar, Stefano Teso, Elia Van Wolputte, Gust Verbruggen, Luc De Raedt:
VisualSynth: Democratizing Data Science in Spreadsheets. ECML/PKDD (5) 2020: 550-554 - 2019
- [c20]Stefano Teso, Kristian Kersting:
Explanatory Interactive Machine Learning. AIES 2019: 239-245 - [c19]Mohit Kumar, Stefano Teso, Patrick De Causmaecker, Luc De Raedt:
Automating Personnel Rostering by Learning Constraints Using Tensors. ICTAI 2019: 697-704 - [c18]Elias Arnold Schede, Samuel Kolb, Stefano Teso:
Learning Linear Programs from Data. ICTAI 2019: 1019-1026 - [c17]Mohit Kumar, Stefano Teso, Luc De Raedt:
Acquiring Integer Programs from Data. IJCAI 2019: 1130-1136 - [c16]Yann Dauxais, Clément Gautrais, Anton Dries, Arcchit Jain, Samuel Kolb, Mohit Kumar, Stefano Teso, Elia Van Wolputte, Gust Verbruggen, Luc De Raedt:
SynthLog: A Language for Synthesising Inductive Data Models (Extended Abstract). PKDD/ECML Workshops (1) 2019: 102-110 - [c15]Stefano Teso:
Constraint Learning: An Appetizer. RW 2019: 232-249 - 2018
- [c14]Paolo Dragone, Stefano Teso, Mohit Kumar, Andrea Passerini:
Decomposition Strategies for Constructive Preference Elicitation. AAAI 2018: 2934-2942 - [c13]Paolo Dragone, Stefano Teso, Andrea Passerini:
Constructive Preference Elicitation Over Hybrid Combinatorial Spaces. AAAI 2018: 2943-2950 - [c12]Luc De Raedt, Andrea Passerini, Stefano Teso:
Learning Constraints From Examples. AAAI 2018: 7965-7970 - [c11]Luc De Raedt, Hendrik Blockeel, Samuel Kolb, Stefano Teso, Gust Verbruggen:
Elements of an Automatic Data Scientist. IDA 2018: 3-14 - [c10]Samuel Kolb, Stefano Teso, Andrea Passerini, Luc De Raedt:
Learning SMT(LRA) Constraints using SMT Solvers. IJCAI 2018: 2333-2340 - [c9]Paolo Dragone, Stefano Teso, Andrea Passerini:
Pyconstruct: Constraint Programming Meets Structured Prediction. IJCAI 2018: 5823-5825 - [c8]Luca Erculiani, Paolo Dragone, Stefano Teso, Andrea Passerini:
Automating Layout Synthesis with Constructive Preference Elicitation. ECML/PKDD (3) 2018: 254-270 - 2017
- [c7]Stefano Teso, Paolo Dragone, Andrea Passerini:
Coactive Critiquing: Elicitation of Preferences and Features. AAAI 2017: 2639-2645 - [c6]Stefano Teso, Andrea Passerini, Paolo Viappiani:
Constructive Preference Elicitation for Multiple Users with Setwise Max-margin. ADT 2017: 3-17 - 2016
- [c5]Stefano Teso, Andrea Passerini, Paolo Viappiani:
Constructive Preference Elicitation by Setwise Max-Margin Learning. IJCAI 2016: 2067-2073 - 2013
- [c4]Stefano Teso, Jacopo Staiano, Bruno Lepri, Andrea Passerini, Fabio Pianesi:
Ego-centric Graphlets for Personality and Affective States Recognition. SocialCom 2013: 874-877 - 2012
- [c3]Elisa Cilia, Stefano Teso, Sergio Ammendola, Tom Lenaerts, Andrea Passerini:
Predicting virus mutations through relational learning. AIMM 2012 - 2010
- [c2]Carlo Nicolini, Bruno Lepri, Stefano Teso, Andrea Passerini:
From On-Going to Complete Activity Recognition Exploiting Related Activities. HBU 2010: 26-37 - [c1]Stefano Teso, Cristina Di Risio, Andrea Passerini, Roberto Battiti:
An On/Off Lattice Approach to Protein Structure Prediction from Contact Maps. PRIB 2010: 368-379
Parts in Books or Collections
- 2023
- [p2]Kareem Ahmed, Stefano Teso, Paolo Morettin, Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Yitao Liang, Eric Wang, Kai-Wei Chang, Andrea Passerini, Guy Van den Broeck:
Semantic Loss Functions for Neuro-Symbolic Structured Prediction. Compendium of Neurosymbolic Artificial Intelligence 2023: 485-505 - 2022
- [p1]Clément Gautrais, Yann Dauxais, Stefano Teso, Samuel Kolb, Gust Verbruggen, Luc De Raedt:
Human-Machine Collaboration for Democratizing Data Science. Human-Like Machine Intelligence 2022: 379-402
Data and Artifacts
- 2022
- [d1]Ivan Donadello, Anthony Hunter, Stefano Teso, Mauro Dragoni:
A Dataset for Utility Prediction in Computational Persuasion with Machine Learning Techniques. Zenodo, 2022
Informal and Other Publications
- 2024
- [i46]Emanuele Marconato, Samuele Bortolotti, Emile van Krieken, Antonio Vergari, Andrea Passerini, Stefano Teso:
BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts. CoRR abs/2402.12240 (2024) - [i45]Debodeep Banerjee, Stefano Teso, Burcu Sayin, Andrea Passerini:
Learning To Guide Human Decision Makers With Vision-Language Models. CoRR abs/2403.16501 (2024) - [i44]Diego Calanzone, Stefano Teso, Antonio Vergari:
Towards Logically Consistent Language Models via Probabilistic Reasoning. CoRR abs/2404.12843 (2024) - [i43]Kareem Ahmed, Stefano Teso, Paolo Morettin, Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Yitao Liang, Eric Wang, Kai-Wei Chang, Andrea Passerini, Guy Van den Broeck:
Semantic Loss Functions for Neuro-Symbolic Structured Prediction. CoRR abs/2405.07387 (2024) - [i42]Samuele Bortolotti, Emanuele Marconato, Tommaso Carraro, Paolo Morettin, Emile van Krieken, Antonio Vergari, Stefano Teso, Andrea Passerini:
A Benchmark Suite for Systematically Evaluating Reasoning Shortcuts. CoRR abs/2406.10368 (2024) - [i41]Steve Azzolin, Antonio Longa, Stefano Teso, Andrea Passerini:
Perks and Pitfalls of Faithfulness in Regular, Self-Explainable and Domain Invariant GNNs. CoRR abs/2406.15156 (2024) - [i40]Cristiana Lalletti, Stefano Teso:
Spurious Correlations in Concept Drift: Can Explanatory Interaction Help? CoRR abs/2407.16515 (2024) - [i39]Diego Calanzone, Stefano Teso, Antonio Vergari:
Logically Consistent Language Models via Neuro-Symbolic Integration. CoRR abs/2409.13724 (2024) - 2023
- [i38]Emanuele Marconato, Gianpaolo Bontempo, Elisa Ficarra, Simone Calderara, Andrea Passerini, Stefano Teso:
Neuro Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal. CoRR abs/2302.01242 (2023) - [i37]Emanuele Marconato, Stefano Teso, Andrea Passerini:
Neuro-Symbolic Reasoning Shortcuts: Mitigation Strategies and their Limitations. CoRR abs/2303.12578 (2023) - [i36]Emanuele Marconato, Stefano Teso, Antonio Vergari, Andrea Passerini:
Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts. CoRR abs/2305.19951 (2023) - [i35]Debodeep Banerjee, Stefano Teso, Andrea Passerini:
Learning to Guide Human Experts via Personalized Large Language Models. CoRR abs/2308.06039 (2023) - [i34]Marc Christiansen, Lea Villadsen, Zhiqiang Zhong, Stefano Teso, Davide Mottin:
How Faithful are Self-Explainable GNNs? CoRR abs/2308.15096 (2023) - [i33]Emanuele Marconato, Andrea Passerini, Stefano Teso:
Interpretability is in the Mind of the Beholder: A Causal Framework for Human-interpretable Representation Learning. CoRR abs/2309.07742 (2023) - 2022
- [i32]Mohit Kumar, Samuel Kolb, Stefano Teso, Luc De Raedt:
Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation. CoRR abs/2202.03888 (2022) - [i31]Stefano Teso, Antonio Vergari:
Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs. CoRR abs/2202.08566 (2022) - [i30]Andrea Bontempelli, Marcelo Rodas Britez, Xiaoyue Li, Haonan Zhao, Luca Erculiani, Stefano Teso, Andrea Passerini, Fausto Giunchiglia:
Lifelong Personal Context Recognition. CoRR abs/2205.10123 (2022) - [i29]Stefano Teso, Laurens Bliek, Andrea Borghesi, Michele Lombardi, Neil Yorke-Smith, Tias Guns, Andrea Passerini:
Machine Learning for Combinatorial Optimisation of Partially-Specified Problems: Regret Minimisation as a Unifying Lens. CoRR abs/2205.10157 (2022) - [i28]Emanuele Marconato, Andrea Passerini, Stefano Teso:
GlanceNets: Interpretabile, Leak-proof Concept-based Models. CoRR abs/2205.15612 (2022) - [i27]Andrea Bontempelli, Stefano Teso, Fausto Giunchiglia, Andrea Passerini:
Concept-level Debugging of Part-Prototype Networks. CoRR abs/2205.15769 (2022) - [i26]Kareem Ahmed, Stefano Teso, Kai-Wei Chang, Guy Van den Broeck, Antonio Vergari:
Semantic Probabilistic Layers for Neuro-Symbolic Learning. CoRR abs/2206.00426 (2022) - [i25]Stefano Teso, Öznur Alkan, Wolfgang Stammer, Elizabeth Daly:
Leveraging Explanations in Interactive Machine Learning: An Overview. CoRR abs/2207.14526 (2022) - 2021
- [i24]Freya Behrens, Stefano Teso, Davide Mottin:
Bandits for Learning to Explain from Explanations. CoRR abs/2102.03815 (2021) - [i23]Antonio Vergari, YooJung Choi, Anji Liu, Stefano Teso, Guy Van den Broeck:
A Compositional Atlas of Tractable Circuit Operations: From Simple Transformations to Complex Information-Theoretic Queries. CoRR abs/2102.06137 (2021) - [i22]Andrea Bontempelli, Fausto Giunchiglia, Andrea Passerini, Stefano Teso:
Human-in-the-loop Handling of Knowledge Drift. CoRR abs/2103.14874 (2021) - [i21]Paolo Dragone, Stefano Teso, Andrea Passerini:
Neuro-Symbolic Constraint Programming for Structured Prediction. CoRR abs/2103.17232 (2021) - [i20]Stefano Teso, Andrea Bontempelli, Fausto Giunchiglia, Andrea Passerini:
Interactive Label Cleaning with Example-based Explanations. CoRR abs/2106.03922 (2021) - [i19]Mohit Kumar, Samuel Kolb, Luc De Raedt, Stefano Teso:
Learning Mixed-Integer Linear Programs from Contextual Examples. CoRR abs/2107.07136 (2021) - [i18]Andrea Bontempelli, Fausto Giunchiglia, Andrea Passerini, Stefano Teso:
Toward a Unified Framework for Debugging Gray-box Models. CoRR abs/2109.11160 (2021) - [i17]Ivan Donadello, Anthony Hunter, Stefano Teso, Mauro Dragoni:
Machine Learning for Utility Prediction in Argument-Based Computational Persuasion. CoRR abs/2112.04953 (2021) - 2020
- [i16]Patrick Schramowski, Wolfgang Stammer, Stefano Teso, Anna Brugger, Xiaoting Shao, Hans-Georg Luigs, Anne-Katrin Mahlein, Kristian Kersting:
Right for the Wrong Scientific Reasons: Revising Deep Networks by Interacting with their Explanations. CoRR abs/2001.05371 (2020) - [i15]Clément Gautrais, Yann Dauxais, Stefano Teso, Samuel Kolb, Gust Verbruggen, Luc De Raedt:
Human-Machine Collaboration for Democratizing Data Science. CoRR abs/2004.11113 (2020) - [i14]Teodora Popordanoska, Mohit Kumar, Stefano Teso:
Toward Machine-Guided, Human-Initiated Explanatory Interactive Learning. CoRR abs/2007.10018 (2020) - [i13]Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Paolo Morettin, Stefano Teso, Andrea Passerini:
Efficient Generation of Structured Objects with Constrained Adversarial Networks. CoRR abs/2007.13197 (2020) - [i12]Teodora Popordanoska, Mohit Kumar, Stefano Teso:
Machine Guides, Human Supervises: Interactive Learning with Global Explanations. CoRR abs/2009.09723 (2020) - [i11]Andrea Bontempelli, Stefano Teso, Fausto Giunchiglia, Andrea Passerini:
Learning in the Wild with Incremental Skeptical Gaussian Processes. CoRR abs/2011.00928 (2020) - [i10]Qiang Shen, Stefano Teso, Wanyi Zhang, Hao Xu, Fausto Giunchiglia:
Multi-Modal Subjective Context Modelling and Recognition. CoRR abs/2011.09671 (2020) - 2019
- [i9]Stefano Teso:
Does Symbolic Knowledge Prevent Adversarial Fooling? CoRR abs/1912.10834 (2019) - 2018
- [i8]Stefano Teso, Kristian Kersting:
"Why Should I Trust Interactive Learners?" Explaining Interactive Queries of Classifiers to Users. CoRR abs/1805.08578 (2018) - [i7]Mohit Kumar, Stefano Teso, Luc De Raedt:
Automating Personnel Rostering by Learning Constraints Using Tensors. CoRR abs/1805.11375 (2018) - 2017
- [i6]Paolo Dragone, Stefano Teso, Andrea Passerini:
Constructive Preference Elicitation over Hybrid Combinatorial Spaces. CoRR abs/1711.07875 (2017) - [i5]Paolo Dragone, Stefano Teso, Mohit Kumar, Andrea Passerini:
Decomposition Strategies for Constructive Preference Elicitation. CoRR abs/1711.08247 (2017) - 2016
- [i4]Stefano Teso, Andrea Passerini, Paolo Viappiani:
Constructive Preference Elicitation by Setwise Max-margin Learning. CoRR abs/1604.06020 (2016) - [i3]Stefano Teso, Paolo Dragone, Andrea Passerini:
Coactive Critiquing: Elicitation of Preferences and Features. CoRR abs/1612.01941 (2016) - 2014
- [i2]Stefano Teso, Roberto Sebastiani, Andrea Passerini:
Hybrid SRL with Optimization Modulo Theories. CoRR abs/1402.4354 (2014) - [i1]Stefano Teso, Roberto Sebastiani, Andrea Passerini:
Structured Learning Modulo Theories. CoRR abs/1405.1675 (2014)
Coauthor Index
[j16] [j14] [j12] [j11] [j10] [j6] [j5] [j4] [j3] [j2] [j1] [c41] [c40] [c39] [c38] [c36] [c34] [c31] [c30] [c29] [c28] [c25] [c23] [c22] [c14] [c13] [c12] [c10] [c9] [c8] [c7] [c6] [c5] [c4] [c3] [c2] [c1] [p2] [i46] [i45] [i43] [i42] [i41] [i38] [i37] [i36] [i35] [i33] [i30] [i29] [i28] [i27] [i22] [i21] [i20] [i18] [i13] [i11] [i6] [i5] [i4] [i3] [i2] [i1]
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