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Creators/Authors contains: "Stromme, Austin"

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  1. ; (, The Annals of Statistics)
    Free, publicly-accessible full text available February 1, 2026
  2. ; ; (, SIAM Journal on Mathematical Analysis)
    Full Text Available 
  3. ; ; ; ; ; (, Proceedings of Machine Learning Research)
    null (Ed.)
    Accepted Manuscript Full Text Available
  4. ; ; ; (, Proceedings of Machine Learning Research)
    Abernethy, Jacob; Agarwal, Shivani (Ed.)
    We study first order methods to compute the barycenter of a probability distribution $$P$$ over the space of probability measures with finite second moment. We develop a framework to derive global rates of convergence for both gradient descent and stochastic gradient descent despite the fact that the barycenter functional is not geodesically convex. Our analysis overcomes this technical hurdle by employing a Polyak-Ł{}ojasiewicz (PL) inequality and relies on tools from optimal transport and metric geometry. In turn, we establish a PL inequality when $$P$$ is supported on the Bures-Wasserstein manifold of Gaussian probability measures. It leads to the first global rates of convergence for first order methods in this context. 
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    Accepted Manuscript Full Text Available
  5. ; ; ; ; ; (, Advances in neural information processing systems)
    null (Ed.)
    Accepted Manuscript Full Text Available