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Creators/Authors contains: "Lu, Yue"

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  1. ; ; ; ; (, NeurIPS 2024 Workshop on Mathematics of Modern Machine Learning (M3L), 2024)
    Free, publicly-accessible full text available October 10, 2025
  2. ; ; ; ; ; (, Physical Review A)
  3. ; ; ; ; ; (, Physical Review Letters)
    Full Text Available 
  4. ; (, IEEE Transactions on Information Theory)
    Full Text Available 
  5. ; ; (, IEEE/CVF Winter Conference on Applications of Computer Vision (WACV))
    Accepted Manuscript Full Text Available
  6. ; ; ; ; (, Data Mining and Knowledge Discovery)
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  7. ; (, IEEE Transactions on Information Theory)
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  8. ; ; ; ; ; ; ; ; ; ; et al (, Cell Reports)
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  9. ; ; (, Journal of Statistical Mechanics: Theory and Experiment)
    Abstract We analyze the dynamics of a random sequential message passing algorithm for approximate inference with large Gaussian latent variable models in a student–teacher scenario. To model nontrivial dependencies between the latent variables, we assume random covariance matrices drawn from rotation invariant ensembles. Moreover, we consider a model mismatching setting, where the teacher model and the one used by the student may be different. By means of dynamical functional approach, we obtain exact dynamical mean-field equations characterizing the dynamics of the inference algorithm. We also derive a range of model parameters for which the sequential algorithm does not converge. The boundary of this parameter range coincides with the de Almeida Thouless (AT) stability condition of the replica-symmetric ansatz for the static probabilistic model. 
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  10. ; ; ; ; (, 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining)
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