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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…
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…
cs.LG2025
Beyond Benign Overfitting in Nadaraya-Watson Interpolators
Daniel Barzilai, Guy Kornowski, Ohad Shamir
In recent years, there has been much interest in understanding the generalization behavior of interpolating predictors, which overfit on noisy training data. Whereas standard analy…