| Comparing Boosting and Bagging for Decision Trees of Rankings | 0 | 0.34 | 2022 |
| Supervised and Reinforcement Learning from Observations in Reconnaissance Blind Chess | 0 | 0.34 | 2022 |
| A flexible class of dependence-aware multi-label loss functions | 0 | 0.34 | 2022 |
| Beyond DNF - First Steps towards Deep Rule Learning. | 0 | 0.34 | 2021 |
| Correlation-Based Discovery of Disease Patterns for Syndromic Surveillance. | 0 | 0.34 | 2021 |
| Rule-Based Multi-label Classification: Challenges and Opportunities | 0 | 0.34 | 2020 |
| Conformal Rule-Based Multi-label Classification | 0 | 0.34 | 2020 |
| Learning to play the Chess Variant Crazyhouse above World Champion Level with Deep Neural Networks and Human Data | 0 | 0.34 | 2020 |
| Ordinal Bucketing for Game Trees using Dynamic Quantile Approximation. | 0 | 0.34 | 2019 |
| Driver Information Embedding With Siamese Lstm Networks | 0 | 0.34 | 2019 |
| Improving the Fusion of Outbreak Detection Methods with Supervised Learning. | 0 | 0.34 | 2019 |
| Learning Analogy-Preserving Sentence Embeddings for Answer Selection | 0 | 0.34 | 2019 |
| Learning Context-dependent Label Permutations for Multi-label Classification | 1 | 0.35 | 2019 |
| The Need for Interpretability Biases. | 0 | 0.34 | 2018 |
| Which Scores to Predict in Sentence Regression for Text Summarization? | 0 | 0.34 | 2018 |
| Informed Hybrid Game Tree Search for General Video Game Playing. | 7 | 0.52 | 2018 |
| On Cognitive Preferences and the Interpretability of Rule-based Models. | 3 | 0.39 | 2018 |
| Leveraging Reproduction-Error Representations for Multi-Instance Classification. | 0 | 0.34 | 2018 |
| A review of possible effects of cognitive biases on interpretation of rule-based machine learning models | 1 | 0.35 | 2018 |
| Batchwise Patching of Classifiers. | 1 | 0.37 | 2018 |
| Learning Interpretable Rules for Multi-label Classification. | 1 | 0.36 | 2018 |
| Towards Semi-Supervised Classification of Event Streams via Denoising Autoencoders | 0 | 0.34 | 2018 |
| Beta Distribution Drift Detection for Adaptive Classifiers. | 0 | 0.34 | 2018 |
| What's Important in a Text? An Extensive Evaluation of Linguistic Annotations for Summarization | 0 | 0.34 | 2018 |
| Preference-Based Monte Carlo Tree Search. | 1 | 0.36 | 2018 |
| Evaluation Of Different Heuristics For Accommodating Asymmetric Loss Functions In Regression | 0 | 0.34 | 2017 |
| Time-to-lane-change prediction with deep learning | 3 | 0.46 | 2017 |
| Multi-objective Optimisation-Based Feature Selection for Multi-label Classification. | 1 | 0.35 | 2017 |
| Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification. | 9 | 0.44 | 2017 |
| Shorter Rules Are Better, Aren't They? | 2 | 0.36 | 2016 |
| Beyond Centrality and Structural Features: Learning Information Importance for Text Summarization. | 2 | 0.37 | 2016 |
| Using semantic similarity for multi-label zero-shot classification of text documents. | 1 | 0.35 | 2016 |
| Model-Free Preference-Based Reinforcement Learning. | 9 | 0.61 | 2016 |
| Special Issue on Discovery Science. | 0 | 0.34 | 2016 |
| All-in Text: Learning Document, Label, and Word Representations Jointly. | 4 | 0.39 | 2016 |
| Sequential Clustering and Contextual Importance Measures for Incremental Update Summarization. | 0 | 0.34 | 2016 |
| On the Importance of a Hierarchy for Learning Continuous Vector Representations of a Label Space. | 0 | 0.34 | 2015 |
| On Learning From Game Annotations | 3 | 0.44 | 2015 |
| Predicting Unseen Labels Using Label Hierarchies in Large-Scale Multi-label Learning | 8 | 0.48 | 2015 |
| Event-based Clustering for Reducing Labeling Costs of Incident-Related Microposts. | 0 | 0.34 | 2015 |
| On the Importance of a Hierarchy for Learning Continuous Vector Representations of a Label Space. | 0 | 0.34 | 2014 |
| Preference Learning from Annotated Game Databases. | 0 | 0.34 | 2014 |
| Graded Multilabel Classification by Pairwise Comparisons | 3 | 0.38 | 2014 |
| Separating Rule Refinement and Rule Selection Heuristics in Inductive Rule Learning. | 8 | 0.47 | 2014 |
| Editorial: Preference learning and ranking | 3 | 0.39 | 2013 |
| Large-Scale Multi-label Text Classification - Revisiting Neural Networks. | 46 | 1.32 | 2013 |
| A Policy Iteration Algorithm for Learning from Preference-Based Feedback. | 2 | 0.41 | 2013 |
| Discovery Science - 16th International Conference, DS 2013, Singapore, October 6-9, 2013. Proceedings | 23 | 0.99 | 2013 |
| Efficient prediction algorithms for binary decomposition techniques | 5 | 0.41 | 2012 |
| Learning from label preferences | 0 | 0.34 | 2011 |
