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Creators/Authors contains: "Balasubramanian, Krishnakumar"

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  1. ; ; (, ICLR)
    Free, publicly-accessible full text available May 20, 2026
  2. ; ; (, Bernoulli)
    Free, publicly-accessible full text available May 20, 2026
  3. ; ; (, Bernoulli)
    Free, publicly-accessible full text available May 1, 2026
  4. ; ; (, SIAM Journal on Optimization)
    Free, publicly-accessible full text available December 31, 2025
  5. ; ; (, Bernoulli)
    Free, publicly-accessible full text available November 1, 2025
  6. ; ; (, 27th International Conference on Artificial Intelligence and Statistics, PMLR)
    Accepted Manuscript Full Text Available
  7. ; ; ; (, Applied and Computational Harmonic Analysis)
    Full Text Available 
  8. ; ; ; (, ICML)
    Accepted Manuscript Full Text Available
  9. ; ; ; (, Foundations of Computational Mathematics)
    Abstract The stein variational gradient descent (SVGD) algorithm is a deterministic particle method for sampling. However, a mean-field analysis reveals that the gradient flow corresponding to the SVGD algorithm (i.e., the Stein Variational Gradient Flow) only provides a constant-order approximation to the Wasserstein gradient flow corresponding to the KL-divergence minimization. In this work, we propose the Regularized Stein Variational Gradient Flow, which interpolates between the Stein Variational Gradient Flow and the Wasserstein gradient flow. We establish various theoretical properties of the Regularized Stein Variational Gradient Flow (and its time-discretization) including convergence to equilibrium, existence and uniqueness of weak solutions, and stability of the solutions. We provide preliminary numerical evidence of the improved performance offered by the regularization. 
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    Full Text Available 
  10. ; ; ; (, Transactions on machine learning research)
    Accepted Manuscript Full Text Available