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Creators/Authors contains: "Cutkosky, Ashok"

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  1. ; ; ; (, Proceedings of the 13th International Conference on Learning Representations (ICLR))
    Free, publicly-accessible full text available April 28, 2026
  2. ; ; ; (, Proceedings of the 13th International Conference on Learning Representations (ICLR))
    Free, publicly-accessible full text available April 28, 2026
  3. ; (, Advances in Neural Information Processing Systems)
    Accepted Manuscript Full Text Available
  4. ; (, Advances in Neural Information Processing Systems)
    Accepted Manuscript Full Text Available
  5. ; ; ; ; ; (, Advances in Neural Information Processing Systems)
    Accepted Manuscript Full Text Available
  6. ; (, International Conference on Machine Learning)
    Accepted Manuscript Full Text Available
  7. ; (, International Conference on Machine Learning)
    Accepted Manuscript Full Text Available
  8. ; ; (, International Conference on Learning Representations)
    Accepted Manuscript Full Text Available
  9. ; ; (, Advances in neural information processing systems (NeurIPS))
    We introduce a technique for tuning the learning rate scale factor of any base optimization algorithm and schedule automatically, which we call Mechanic. Our method provides a practical realization of recent theoretical reductions for accomplishing a similar goal in online convex optimization. We rigorously evaluate Mechanic on a range of large scale deep learning tasks with varying batch sizes, schedules, and base optimization algorithms. These experiments demonstrate that depending on the problem, Mechanic either comes very close to, matches or even improves upon manual tuning of learning rates. 
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    Accepted Manuscript Full Text Available
  10. ; (, Proceedings of Machine Learning Research)