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Award ID contains: 2120018

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  1. ; ; (, ICLR)
    Free, publicly-accessible full text available April 1, 2026
  2. ; ; (, ICML 2024)
    Accepted Manuscript Full Text Available
  3. ; ; (, ICML 2024)
    Accepted Manuscript Full Text Available
  4. ; ; (, CVPR 2024)
    Accepted Manuscript Full Text Available
  5. ; ; (, CVPR 2024)
    Accepted Manuscript Full Text Available
  6. ; ; (, AISTATS 2024)
    Accepted Manuscript Full Text Available
  7. ; ; (, AISTATS 2024)
    Accepted Manuscript Full Text Available
  8. ; ; ; ; (, Arxiv)
    Accepted Manuscript Full Text Available
  9. (, NeurIPS 2022 Workshop on Federated Learning)
    Accepted Manuscript Full Text Available
  10. (, Transaction Machine Learning Research)
    Hybrid modelling reduces the misspecification of expert models by combining them with machine learning (ML) components learned from data. Similarly to many ML algorithms, hybrid model performance guarantees are limited to the training distribution. Leveraging the insight that the expert model is usually valid even outside the training domain, we overcome this limitation by introducing a hybrid data augmentation strategy termed expert augmentation. Based on a probabilistic formalization of hybrid modelling, we demonstrate that expert augmentation, which can be incorporated into existing hybrid systems, improves generalization. We empirically validate the expert augmentation on three controlled experiments modelling dynamical systems with ordinary and partial differential equations. Finally, we assess the potential real-world applicability of expert augmentation on a dataset of a real double pendulum. 
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    Accepted Manuscript Full Text Available