5 citations · 18 across the 18 of their papers we have counts for
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Efficient Agnostic Learning with Average Smoothness
Steve Hanneke, Aryeh Kontorovich, Guy Kornowski
We study distribution-free nonparametric regression following a notion of average smoothness initiated by Ashlagi et al. (2021), which measures the "effective" smoothness of a func…
An Algorithm with Optimal Dimension-Dependence for Zero-Order Nonsmooth Nonconvex Stochastic Optimization
Guy Kornowski, Ohad Shamir
We study the complexity of producing -stationary points of Lipschitz objectives which are possibly neither smooth nor convex, using only noisy function evaluations. Recent w…
From Tempered to Benign Overfitting in ReLU Neural Networks
Guy Kornowski, Gilad Yehudai, Ohad Shamir
Overparameterized neural networks (NNs) are observed to generalize well even when trained to perfectly fit noisy data. This phenomenon motivated a large body of work on "benign ove…
Deterministic Nonsmooth Nonconvex Optimization
Michael I. Jordan, Guy Kornowski, Tianyi Lin +2
We study the complexity of optimizing nonsmooth nonconvex Lipschitz functions by producing -stationary points. Several recent works have presented randomized algorithms that…
Near-optimal learning with average Hölder smoothness
Steve Hanneke, Aryeh Kontorovich, Guy Kornowski
We generalize the notion of average Lipschitz smoothness proposed by Ashlagi et al. (COLT 2021) by extending it to Hölder smoothness. This measure of the "effective smoothness" of…