| Learning MAX-SAT from contextual examples for combinatorial optimisation | 0 | 0.34 | 2023 |
| Machine Learning for Utility Prediction in Argument-Based Computational Persuasion. | 0 | 0.34 | 2022 |
| Federated Multi-Task Attention for Cross-Individual Human Activity Recognition. | 0 | 0.34 | 2022 |
| Federated Multi-Task Attention for Cross-Individual Human Activity Recognition | 0 | 0.34 | 2022 |
| Neuro-Symbolic Constraint Programming for Structured Prediction | 0 | 0.34 | 2021 |
| Learning Modulo Theories for constructive preference elicitation | 0 | 0.34 | 2021 |
| Predictive spreadsheet autocompletion with constraints | 0 | 0.34 | 2020 |
| Challenges in Interactive Machine Learning. | 0 | 0.34 | 2020 |
| Learning Max-Sat From Contextual Examples For Combinatorial Optimisation | 0 | 0.34 | 2020 |
| Making Deep Neural Networks Right For The Right Scientific Reasons By Interacting With Their Explanations | 4 | 0.53 | 2020 |
| Efficient Generation of Structured Objects with Constrained Adversarial Networks | 0 | 0.34 | 2020 |
| Learning Weighted Model Integration Distributions | 0 | 0.34 | 2020 |
| Learning in the Wild with Incremental Skeptical Gaussian Processes | 0 | 0.34 | 2020 |
| Multi-Modal Subjective Context Modelling and Recognition | 0 | 0.34 | 2020 |
| Constraint Learning - An Appetizer. | 0 | 0.34 | 2019 |
| Combining learning and constraints for genome-wide protein annotation. | 0 | 0.34 | 2019 |
| Acquiring Integer Programs from Data. | 0 | 0.34 | 2019 |
| Learning Linear Programs From Data | 0 | 0.34 | 2019 |
| Automating Personnel Rostering By Learning Constraints Using Tensors | 0 | 0.34 | 2019 |
| SynthLog - A Language for Synthesising Inductive Data Models (Extended Abstract). | 0 | 0.34 | 2019 |
| Constructive Preference Elicitation over Hybrid Combinatorial Spaces | 1 | 0.35 | 2018 |
| Constructive Preference Elicitation. | 0 | 0.34 | 2018 |
| Decomposition Strategies for Constructive Preference Elicitation | 2 | 0.36 | 2018 |
| Pyconstruct: Constraint Programming Meets Structured Prediction. | 0 | 0.34 | 2018 |
| Learning SMT(LRA) Constraints using SMT Solvers. | 0 | 0.34 | 2018 |
| Elements of an Automatic Data Scientist. | 0 | 0.34 | 2018 |
| Learning Constraints From Examples. | 2 | 0.45 | 2018 |
| Automating Personnel Rostering by Learning Constraints Using Tensors. | 0 | 0.34 | 2018 |
| Coactive Critiquing: Elicitation of Preferences and Features. | 1 | 0.34 | 2017 |
| Constructive Preference Elicitation for Multiple Users with Setwise Max-margin. | 2 | 0.36 | 2017 |
| Constructive Preference Elicitation by Setwise Max-margin Learning. | 4 | 0.40 | 2016 |
| Structured Learning Modulo Theories. | 4 | 0.42 | 2014 |
| Improved multi-level protein-protein interaction prediction with semantic-based regularization. | 6 | 0.38 | 2014 |
| Joint probabilistic-logical refinement of multiple protein feature predictors. | 0 | 0.34 | 2014 |
| Predicting virus mutations through statistical relational learning. | 5 | 0.38 | 2014 |
| Hybrid SRL with Optimization Modulo Theories. | 1 | 0.36 | 2014 |
| Ego-centric Graphlets for Personality and Affective States Recognition | 3 | 0.36 | 2013 |
| Predicting virus mutations through relational learning. | 1 | 0.35 | 2012 |
| From on-going to complete activity recognition exploiting related activities | 2 | 0.37 | 2010 |
| An on/off lattice approach to protein structure prediction from contact maps | 0 | 0.34 | 2010 |
