1 citations · 1 across the 6 of their papers we have counts for
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Trade-offs in Data Memorization via Strong Data Processing Inequalities
Vitaly Feldman, Guy Kornowski, Xin Lyu
Recent research demonstrated that training large language models involves memorization of a significant fraction of training data. Such memorization can lead to privacy violations…
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…
Improved Sample Complexity for Private Nonsmooth Nonconvex Optimization
Guy Kornowski, Daogao Liu, Kunal Talwar
We study differentially private (DP) optimization algorithms for stochastic and empirical objectives which are neither smooth nor convex, and propose methods that return a Goldstei…
Differentially Private Bilevel Optimization
Guy Kornowski
We present differentially private (DP) algorithms for bilevel optimization, a problem class that received significant attention lately in various machine learning applications. The…
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…
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…