Intro
Preface
Organization
Keynote Talks
Unsupervised Model Selection in Outlier Detection: The Elephant in the Room
Coloring Social Relationships
35 Years of 'Scientific Discovery: Computational Explorations of the Creative Processes' - From the Early Days to the State of the Art
Contents
Regression and Limited Data
Model Optimization in Imbalanced Regression
1 Introduction
2 Related Work
3 Imbalanced Regression
3.1 Relevance Function
3.2 Squared Error Relevance Area (SERA)
4 Optimization Loss Function for Imbalanced Regression

5 Experimental Study
5.1 Experimental Setup
5.2 Results on Model Optimization
5.3 Results in Out-of-Sample
6 Conclusions
A SERA numerical approximation
B Tables of Results
References
Discovery of Differential Equations Using Probabilistic Grammars
1 Introduction
2 Related Work
3 Methods
3.1 Algebraic Equations and Numeric Differentiation
3.2 Differential Equations and Direct Simulation
3.3 Parallel Computation
4 Experimental Evaluation
4.1 Experimental Setup
4.2 Results
5 Conclusion
References

Hyperparameter Importance of Quantum Neural Networks Across Small Datasets
1 Introduction
2 Background
2.1 Functional ANOVA
2.2 Supervised Learning with Parameterized Quantum Circuits
3 Methods
3.1 Hyperparameters and Configuration Space
3.2 Assessing Hyperparameter Importance
3.3 Verifying Hyperparameter Importance
4 Dataset and Inclusion Criteria
5 Results
5.1 Performance Distributions per Dataset
5.2 Surrogate Verification
5.3 Marginal Contributions
5.4 Random Search Verification
6 Conclusion
References

ImitAL: Learned Active Learning Strategy on Synthetic Data
1 Introduction
2 Simulating AL on Synthetic Training Data
3 Training a Neural Network by Imitation Learning
3.1 Imitation Learning
3.2 Neural Network Input and Output Encoding
3.3 Pre-selection
4 Evaluation
4.1 Experiment Details
4.2 Comparison with Other Active Learning Strategies
5 Conclusion
References
Incremental/Continual Learning
Predicting Potential Real-Time Donations in YouTube Live Streaming Services via Continuous-Time Dynamic Graph
1 Introduction
2 Related Work

2.1 Online Live Streaming Service
2.2 Dynamic Graph Learning
3 Methodology
3.1 Dataset
3.2 Dynamic Graph Generation
3.3 Temporal Graph Neural Network
3.4 Strategies for Data Imbalance
4 Experiments
4.1 Dataset Description
4.2 Experiment Setup
4.3 Baselines
4.4 Evaluation
4.5 Case Study
5 Conclusion
References
Semi-supervised Change Point Detection Using Active Learning
1 Introduction
2 AL-CPD
2.1 Algorithm Outline
2.2 Selecting Candidate Change Points
2.3 Finding New Candidate Change Points
3 Experiments
3.1 Datasets