Alphabetically
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Discrepancy measures and accelerated likelihood-free inference for simulator-based models
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Exploration and control of the inner representation in generative AI models
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Learning differential models (and other logic rules) from data with uncertainty
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Multi-modal data integration for personalized treatment recommendations
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Scalable and knowledge-driven identification of anomalous structure in evolving data stream settings
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Sparse models for machine learning from a Bayesian viewpoint
- Statistical models and logic: handling inconsistencies
- Structure-generating models for transition metal complexes
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Synthetic data generation balancing privacy and utility, using vine copulas
- Uncertainty quantification in the presence of logical constraints
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Understanding the generalization capabilities of neural networks through information theory