Author

G. Buzaglo

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#machine learning Preprint Oct 2026

Optimal Algorithms for Nonsmooth Nonconvex Stochastic Optimization with Constraints

We present new algorithms for stochastic optimization problems in which the objective is neither smooth nor convex, subject to a convex constraint set. Our algorithms return a $(\delta,\varepsilon)$-Goldstein stationary point, as recently defined for constrained problems by Liu et al. (2024), with improved complexities...

G. Buzaglo, Guy Kornowski · 0 citations

#machine learning Preprint Sep 2026

Oracle-Efficient Online Classification with Stochastic Inputs and Adversarial Outputs

It is shown that a simple Follow-the-Perturbed-Leader algorithm with Gaussian perturbation for each observed context achieves the optimal $\widetilde O(\sqrt{T\log N})$ expected regret for a class of $N$ experts, while requiring one optimization-oracle call per round and no explicit enumeration of the class.

G. Buzaglo, Elad Hazan · 1 citation