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Creators/Authors contains: "Willett, Rebecca"

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  1. ; ; ; ; (, Proceedings of the 41st International Conference on Machine Learning)
    Free, publicly-accessible full text available July 22, 2025
  2. ; ; ; ; (, 37th Annual Conference on Learning Theory)
    Free, publicly-accessible full text available June 30, 2025
  3. ; ; (, AISTATS, Proceedings of Machine Learning Research)
    Accepted Manuscript Full Text Available
  4. ; ; (, Journal of machine learning research)
    Accepted Manuscript Full Text Available
  5. ; ; (, Statistica Sinica)
    Full Text Available 
  6. ; ; ; ; (, COLT)
    Accepted Manuscript Full Text Available
  7. ; ; ; (, 37th Conference on Neural Information Processing Systems (NeurIPS 2023))
    Accepted Manuscript Full Text Available
  8. ; ; ; (, NeurIPS)
    Accepted Manuscript Full Text Available
  9. ; ; (, Inverse Problems)
    Abstract This paper introduces a computational framework to reconstruct and forecast a partially observed state that evolves according to an unknown or expensive-to-simulate dynamical system. Our reduced-order autodifferentiable ensemble Kalman filters (ROAD-EnKFs) learn a latent low-dimensional surrogate model for the dynamics and a decoder that maps from the latent space to the state space. The learned dynamics and decoder are then used within an EnKF to reconstruct and forecast the state. Numerical experiments show that if the state dynamics exhibit a hidden low-dimensional structure, ROAD-EnKFs achieve higher accuracy at lower computational cost compared to existing methods. If such structure is not expressed in the latent state dynamics, ROAD-EnKFs achieve similar accuracy at lower cost, making them a promising approach for surrogate state reconstruction and forecasting. 
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  10. ; ; (, Transactions on Machine Learning Research)
    Accepted Manuscript Full Text Available