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  1. ; ; ; (, Journal of the ACM)
    We show that any memory-constrained, first-order algorithm which minimizes d-dimensional, 1-Lipschitz convex functions over the unit ball to 1/poly(d) accuracy using at most $$d^{1.25-\delta}$$ bits of memory must make at least $$\tilde{Omega}(d^{1+(4/3)\delta})$$ first-order queries (for any constant $$\delta in [0,1/4]$$). Consequently, the performance of such memory-constrained algorithms are a polynomial factor worse than the optimal $$\tilde{O}(d)$$ query bound for this problem obtained by cutting plane methods that use $$\tilde{O}(d^2)$$ memory. This resolves a COLT 2019 open problem of Woodworth and Srebro. 
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    Free, publicly-accessible full text available December 31, 2025
  2. ; ; ; ; (, The Annals of Statistics)
    Free, publicly-accessible full text available December 1, 2025
  3. ; ; ; (, The Thirty-eighth Annual Conference on Neural Information Processing Systems)
    Free, publicly-accessible full text available December 1, 2025
  4. ; ; (, 16th Annual Workshop on Optimization for Machine Learning)
    Free, publicly-accessible full text available December 1, 2025
  5. ; ; ; ; (, The Thirty-eighth Annual Conference on Neural Information Processing Systems)
    Free, publicly-accessible full text available December 1, 2025
  6. ; ; (, The Thirty-eighth Annual Conference on Neural Information Processing Systems)
    Free, publicly-accessible full text available December 1, 2025
  7. ; ; ; (, The Thirty-eighth Annual Conference on Neural Information Processing Systems)
    Free, publicly-accessible full text available December 1, 2025
  8. ; ; ; (, The Thirty-eighth Annual Conference on Neural Information Processing Systems)
    Free, publicly-accessible full text available December 1, 2025
  9. ; ; (, Transactions on Machine Learning Research)
    Accepted Manuscript Full Text Available
  10. ; ; ; (, Proceedings of the 41st International Conference on Machine Learning)
    Accepted Manuscript Full Text Available