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Creators/Authors contains: "Telgarsky, Matus"

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  1. ; ; ; (, openreview)
    Free, publicly-accessible full text available July 19, 2026
  2. ; ; ; (, Proceedings of the 37th Conference on Learning Theory (COLT2024))
    Accepted Manuscript Full Text Available
  3. ; ; (, International Conference on learning representations)
    Accepted Manuscript Full Text Available
  4. ; ; (, Proceedings of Machine Learning Research)
    null (Ed.)
    We present and analyze a momentum-based gradient method for training linear classifiers with an exponentially-tailed loss (eg, the exponential or logistic loss), which maximizes the classification margin on separable data at a rate of O (1/t^ 2). This contrasts with a rate of O (1/log (t)) for standard gradient descent, and O (1/t) for normalized gradient descent. The momentum-based method is derived via the convex dual of the maximum-margin problem, and specifically by applying Nesterov acceleration to this dual, which manages to result in a simple and intuitive method in the primal. This dual view can also be used to derive a stochastic variant, which performs adaptive non-uniform sampling via the dual variables. 
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    Accepted Manuscript Full Text Available
  5. ; ; (, International Conference on Machine Learning)
    Accepted Manuscript Full Text Available
  6. ; ; (, Advances in neural information processing systems)
    Accepted Manuscript Full Text Available
  7. ; ; (, international conference on machine learning)
    Accepted Manuscript Full Text Available
  8. ; ; (, International Conference on Web and Internet Economics)
    Accepted Manuscript Full Text Available