1.9k citations · 6.5k across the 132 of their papers we have counts for
12 papers · 1 filter
Reducing Adversarially Robust Learning to Non-Robust PAC Learning
Omar Montasser, Steve Hanneke, Nathan Srebro
We study the problem of reducing adversarially robust learning to standard PAC learning, i.e. the complexity of learning adversarially robust predictors using access to only a blac…
Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy
Edward Moroshko, Suriya Gunasekar, Blake Woodworth +3
We provide a detailed asymptotic study of gradient flow trajectories and their implicit optimization bias when minimizing the exponential loss over "diagonal linear networks". This…
Predictive Value Generalization Bounds
Keshav Vemuri, Nathan Srebro
In this paper, we study a bi-criterion framework for assessing scoring functions in the context of binary classification. The positive and negative predictive values (ppv and npv,…
On Uniform Convergence and Low-Norm Interpolation Learning
Lijia Zhou, Danica J. Sutherland, Nathan Srebro
We consider an underdetermined noisy linear regression model where the minimum-norm interpolating predictor is known to be consistent, and ask: can uniform convergence in a norm ba…
Minibatch vs Local SGD for Heterogeneous Distributed Learning
Blake Woodworth, Kumar Kshitij Patel, Nathan Srebro
We analyze Local SGD (aka parallel or federated SGD) and Minibatch SGD in the heterogeneous distributed setting, where each machine has access to stochastic gradient estimates for…
Efficiently Learning Adversarially Robust Halfspaces with Noise
Omar Montasser, Surbhi Goel, Ilias Diakonikolas +1
We study the problem of learning adversarially robust halfspaces in the distribution-independent setting. In the realizable setting, we provide necessary and sufficient conditions…