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Creators/Authors contains: "Woodworth Blake"

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  1. ; ; (, International Conference on Machine Learning)
    Free, publicly-accessible full text available July 23, 2026
  2. ; ; (, International Conference on Learning Representations)
    Free, publicly-accessible full text available March 1, 2026
  3. ; ; ; ; ; (, Mathematical Programming)
    Full Text Available 
  4. ; (, Advances in neural information processing systems)
    Accepted Manuscript Full Text Available
  5. ; ; (, Proceedings of Machine Learning Research)
    null (Ed.)
    We present a direct (primal only) derivation of Mirror Descent as a “partial” discretization of gradient flow on a Riemannian manifold where the metric tensor is the Hessian of the Mirror Descent potential function. We contrast this discretization to Natural Gradient Descent, which is obtained by a “full” forward Euler discretization. This view helps shed light on the relationship between the methods and allows generalizing Mirror Descent to any Riemannian geometry in Rd, even when the metric tensor is not a Hessian, and thus there is no “dual.” 
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    Accepted Manuscript Full Text Available
  6. ; ; ; ; ; ; (, Proceedings of Machine Learning Research)
    null (Ed.)
    Recent work has highlighted the role of initialization scale in determining the structure of the solutions that gradient methods converge to. In particular, it was shown that large initialization leads to the neural tangent kernel regime solution, whereas small initialization leads to so called “rich regimes”. However, the initialization structure is richer than the overall scale alone and involves relative magnitudes of different weights and layers in the network. Here we show that these relative scales, which we refer to as initialization shape, play an important role in determining the learned model. We develop a novel technique for deriving the inductive bias of gradientflow and use it to obtain closed-form implicit regularizers for multiple cases of interest. 
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    Accepted Manuscript Full Text Available
  7. ; ; ; (, Conference on Learning Theory)
    Accepted Manuscript Full Text Available
  8. ; ; ; ; (, Advances in neural information processing systems)
    Accepted Manuscript Full Text Available
  9. ; ; (, Advances in Neural Information Processing Systems 33 (NeurIPS 2020))
    null (Ed.)
    Accepted Manuscript Full Text Available
  10. ; ; ; ; ; (, Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics)
    null; null; null; null (Ed.)
    Accepted Manuscript Full Text Available