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Creators/Authors contains: "Sharan, Vatsal"

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  1. ; ; (, Advances in Neural Information Processing Systems)
    Free, publicly-accessible full text available December 1, 2026
  2. ; ; (, Advances in Neural Information Processing Systems)
    Free, publicly-accessible full text available December 1, 2026
  3. ; ; ; (, Advances in Neural Information Processing Systems)
    Free, publicly-accessible full text available December 1, 2026
  4. ; ; ; ; (, Advances in Neural Information Processing Systems)
    Free, publicly-accessible full text available December 1, 2026
  5. ; ; ; ; (, Annual AAAI Conference on Artificial Intelligence)
    Free, publicly-accessible full text available December 1, 2026
  6. ; ; (, Artificial Intelligence and Statistics)
    Free, publicly-accessible full text available April 1, 2026
  7. ; ; ; ; ; (, International Conference on Learning Representations)
    Free, publicly-accessible full text available April 1, 2026
  8. ; ; ; ; (, Proceedings of the 36th International Conference on Algorithmic Learning Theory (ALT 2025))
    Free, publicly-accessible full text available February 27, 2026
  9. ; ; ; ; (, International Conference on Algorithmic Learning Theory)
    Free, publicly-accessible full text available February 1, 2026
  10. ; ; ; (, 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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