Stefano Teso

Proceedings of the Joint Proceedings of the ACM IUI 2021 Workshops co-located with 26th ACM Conference on Intelligent User Interfaces (ACM IUI 2021), 2021

2020

Making deep neural networks right for the right scientific reasons by interacting with their explanations.

[BibT_eX]

[DOI]

Nat. Mach. Intell., 2020

Predictive spreadsheet autocompletion with constraints.

[BibT_eX]

[DOI]

Mach. Learn., 2020

Challenges in Interactive Machine Learning.

[BibT_eX]

[DOI]

Oliver Hinz

Künstliche Intell., 2020

Machine Guides, Human Supervises: Interactive Learning with Global Explanations.

[BibT_eX]

[DOI]

Teodora Popordanoska

CoRR, 2020

Toward Machine-Guided, Human-Initiated Explanatory Interactive Learning.

[BibT_eX]

[DOI]

Teodora Popordanoska

CoRR, 2020

Human-Machine Collaboration for Democratizing Data Science.

[BibT_eX]

[DOI]

CoRR, 2020

Right for the Wrong Scientific Reasons: Revising Deep Networks by Interacting with their Explanations.

[BibT_eX]

[DOI]

CoRR, 2020

VisualSynth: Democratizing Data Science in Spreadsheets.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track, 2020

Efficient Generation of Structured Objects with Constrained Adversarial Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning in the Wild with Incremental Skeptical Gaussian Processes.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Multi-Modal Subjective Context Modelling and Recognition.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Workshop Modelling and Reasoning in Context co-located with the 24th European Conference on Artificial Intelligence, 2020

Learning Weighted Model Integration Distributions.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Does Symbolic Knowledge Prevent Adversarial Fooling?

[BibT_eX]

[DOI]

CoRR, 2019

Combining learning and constraints for genome-wide protein annotation.

[BibT_eX]

[DOI]

Luca Masera

Michelangelo Diligenti

BMC Bioinform., 2019

Constraint Learning: An Appetizer.

[BibT_eX]

[DOI]

Proceedings of the Reasoning Web. Explainable Artificial Intelligence, 2019

SynthLog: A Language for Synthesising Inductive Data Models (Extended Abstract).

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

Acquiring Integer Programs from Data.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Learning Linear Programs from Data.

[BibT_eX]

[DOI]

Elias Arnold Schede

Samuel Kolb

Proceedings of the 31st IEEE International Conference on Tools with Artificial Intelligence, 2019

Automating Personnel Rostering by Learning Constraints Using Tensors.

[BibT_eX]

[DOI]

Patrick De Causmaecker

Proceedings of the 31st IEEE International Conference on Tools with Artificial Intelligence, 2019

Explanatory Interactive Machine Learning.

[BibT_eX]

[DOI]

Kristian Kersting

Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019

2018

Automating Personnel Rostering by Learning Constraints Using Tensors.

[BibT_eX]

[DOI]

CoRR, 2018

"Why Should I Trust Interactive Learners?" Explaining Interactive Queries of Classifiers to Users.

[BibT_eX]

[DOI]

Kristian Kersting

CoRR, 2018

Automating Layout Synthesis with Constructive Preference Elicitation.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018

Learning SMT(LRA) Constraints using SMT Solvers.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Pyconstruct: Constraint Programming Meets Structured Prediction.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Elements of an Automatic Data Scientist.

[BibT_eX]

[DOI]

Proceedings of the Advances in Intelligent Data Analysis XVII, 2018

Learning Constraints From Examples.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

Constructive Preference Elicitation Over Hybrid Combinatorial Spaces.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

Decomposition Strategies for Constructive Preference Elicitation.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Constructive Preference Elicitation.

[BibT_eX]

[DOI]

Frontiers Robotics AI, 2017

Structured learning modulo theories.

[BibT_eX]

[DOI]

Roberto Sebastiani

Artif. Intell., 2017

Constructive Preference Elicitation for Multiple Users with Setwise Max-margin.

[BibT_eX]

[DOI]

Paolo Viappiani

Proceedings of the Algorithmic Decision Theory - 5th International Conference, 2017

Coactive Critiquing: Elicitation of Preferences and Features.

[BibT_eX]

[DOI]

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Constructive Preference Elicitation by Setwise Max-Margin Learning.

[BibT_eX]

[DOI]

Paolo Viappiani

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

2014

Hybrid SRL with Optimization Modulo Theories.

[BibT_eX]

[DOI]

Roberto Sebastiani

CoRR, 2014

Joint probabilistic-logical refinement of multiple protein feature predictors.

[BibT_eX]

[DOI]

BMC Bioinform., 2014

Improved multi-level protein¿protein interaction prediction with semantic-based regularization.

[BibT_eX]

[DOI]

Claudio Saccà

Michelangelo Diligenti

BMC Bioinform., 2014

Predicting virus mutations through statistical relational learning.

[BibT_eX]

[DOI]

BMC Bioinform., 2014

2013

Statistical Relational Learning for Proteomics: Function, Interactions and Evolution.

[BibT_eX]

[DOI]