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cs.LG2026
Benign Overfitting for General Norms and Distributions
Daniel Barzilai, Ohad Shamir
Understanding why predictors can generalize despite interpolating noisy training data is a central puzzle in machine learning. Most work on such "benign overfitting" studies minimu…
math.OC2026
Gradient Descent's Last Iterate is Often (slightly) Suboptimal
Guy Kornowski, Ohad Shamir
We consider the well-studied setting of minimizing a convex Lipschitz function using either gradient descent (GD) or its stochastic variant (SGD), and examine the last iterate conv…
cs.LG2026
Limitations of SGD for Multi-Index Models Beyond Statistical Queries
Daniel Barzilai, Ohad Shamir
Understanding the limitations of gradient methods, and stochastic gradient descent (SGD) in particular, is a central challenge in learning theory. To that end, a commonly used tool…