PhD student, Tel Aviv University
3 papers at NeurIPS 2025
We present a sharp last-iterate analysis of SGD on smooth convex losses in the interpolation regime, extending prior results beyond linear regression and improving known rates for large, constant stepsizes.
We prove that using regularization with either fixed or increasing strength yields near-optimal and optimal worst-case expected loss rates in realizable continual regression under random task orderings.
