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Creators/Authors contains: "Rey-Bellet, Luc"

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  1. ; ; ; ; ( , SIAM Journal on Mathematics of Data Science)
    Free, publicly-accessible full text available December 31, 2025
  2. ; ; ; ( , Proceedings of the 40th International Conference on Machine Learning)
    Accepted Manuscript Full Text Available
  3. ; ; ; ; ( , ICLR 2023)
    Accepted Manuscript Full Text Available
  4. ; ; ; ( , SIAM/ASA Journal on Uncertainty Quantification)
    Full Text Available 
  5. ; ; ; ( , Proceedings of Machine Learning Research)
    Accepted Manuscript Full Text Available
  6. ; ; ; ( , ICML 2022)
    Accepted Manuscript Full Text Available
  7. ; ; ; ; ( , Journal of machine learning research)
    Accepted Manuscript Full Text Available
  8. ; ; ; ; ( , Journal of machine learning research)
    Accepted Manuscript Full Text Available
  9. ; ; ( , ESAIM: Mathematical Modelling and Numerical Analysis)
    Quantifying the impact of parametric and model-form uncertainty on the predictions of stochastic models is a key challenge in many applications. Previous work has shown that the relative entropy rate is an effective tool for deriving path-space uncertainty quantification (UQ) bounds on ergodic averages. In this work we identify appropriate information-theoretic objects for a wider range of quantities of interest on path-space, such as hitting times and exponentially discounted observables, and develop the corresponding UQ bounds. In addition, our method yields tighter UQ bounds, even in cases where previous relative-entropy-based methods also apply, e.g. , for ergodic averages. We illustrate these results with examples from option pricing, non-reversible diffusion processes, stochastic control, semi-Markov queueing models, and expectations and distributions of hitting times. 
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  10. ; ; ; ; ( , SIAM journal on mathematics of data science)
    null (Ed.)
    Accepted Manuscript Full Text Available