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Creators/Authors contains: "Khan, Mohammad Emtiyaz"

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  1. ; ; ; ; ; ; ; (, NeurIPS)
    Free, publicly-accessible full text available September 18, 2026
  2. ; ; (, International Conference on Learning Representations)
    Free, publicly-accessible full text available April 24, 2026
  3. ; ; (, ICLR Conference)
    We provide new connections between two distinct federated learning approaches based on (i) ADMM and (ii) Variational Bayes (VB), and propose new variants by combining their complementary strengths. Specifically, we show that the dual variables in ADMM naturally emerge through the "site" parameters used in VB with isotropic Gaussian covariances. Using this, we derive two versions of ADMM from VB that use flexible covariances and functional regularisation, respectively. Through numerical experiments, we validate the improvements obtained in performance. The work shows connection between two fields that are believed to be fundamentally different and combines them to improve federated learning. 
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    Free, publicly-accessible full text available January 22, 2026
  4. ; ; ; ; ; (, Proceedings of Machine Learning Research)
    Riemannian submanifold optimization with momentum is computationally challenging because, to ensure that the iterates remain on the submanifold, we often need to solve difficult differential equations. Here, we simplify such difficulties for a class of structured symmetric positive-definite matrices with the affine-invariant metric. We do so by proposing a generalized version of the Riemannian normal coordinates that dynamically orthonormalizes the metric and locally converts the problem into an unconstrained problem in the Euclidean space. We use our approach to simplify existing approaches for structured covariances and develop matrix-inverse-free 2nd-order optimizers for deep learning in low precision settings. 
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    Accepted Manuscript Full Text Available