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Creators/Authors contains: "Maunu, Tyler"

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  1. ; (, Proceedings of Machine Learning Research)
    Dasgupta, Sanjoy; Mandt, Stephan; Li, Yingzhen (Ed.)
    We study accelerated optimization methods in the Gaussian phase retrieval problem. In this setting, we prove that gradient methods with Polyak or Nesterov momentum have similar implicit regularization to gradient descent. This implicit regularization ensures that the algorithms remain in a nice region, where the cost function is strongly convex and smooth despite being nonconvex in general. This ensures that these accelerated methods achieve faster rates of convergence than gradient descent. Experimental evidence demonstrates that the accelerated methods converge faster than gradient descent in practice. 
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    Accepted Manuscript Full Text Available
  2. ; (, International Journal of Computer Vision)
    Full Text Available 
  3. ; ; (, Proceedings of Machine Learning Research)
    Accepted Manuscript Full Text Available
  4. ; ; (, Mathematical and Scientific Machine Learning)
    Accepted Manuscript Full Text Available
  5. ; ; (, Advances in neural information processing systems)
    Accepted Manuscript Full Text Available
  6. ; ; ; (, 2021 International Conference on 3D Vision (3DV))
    Full Text Available 
  7. ; ; (, Advances in neural information processing systems)
    Accepted Manuscript Full Text Available
  8. ; ; ; (, 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
  9. ; ; ; ; (, Advances in neural information processing systems)
    null (Ed.)
    Accepted Manuscript Full Text Available
  10. ; ; ; ; ; (, Advances in neural information processing systems)
    null (Ed.)
    Accepted Manuscript Full Text Available