1.9k citations · 6.5k across the 132 of their papers we have counts for
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A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models
Lijia Zhou, Frederic Koehler, Pragya Sur +2
We prove a new generalization bound that shows for any class of linear predictors in Gaussian space, the Rademacher complexity of the class and the training error under any continu…
Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data
Spencer Frei, Gal Vardi, Peter L. Bartlett +2
The implicit biases of gradient-based optimization algorithms are conjectured to be a major factor in the success of modern deep learning. In this work, we investigate the implicit…
Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization
Omar Montasser, Steve Hanneke, Nathan Srebro
We present a minimax optimal learner for the problem of learning predictors robust to adversarial examples at test-time. Interestingly, we find that this requires new algorithmic i…
Pessimism for Offline Linear Contextual Bandits using Confidence Sets
Gene Li, Cong Ma, Nathan Srebro
We present a family of pessimistic learning rules for offline learning of linear contextual bandits, relying on confidence sets with respect to different $\ell…
Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization
Idan Amir, Roi Livni, Nathan Srebro
We consider linear prediction with a convex Lipschitz loss, or more generally, stochastic convex optimization problems of generalized linear form, i.e.~where each instantaneous los…
The Sample Complexity of One-Hidden-Layer Neural Networks
Gal Vardi, Ohad Shamir, Nathan Srebro
We study norm-based uniform convergence bounds for neural networks, aiming at a tight understanding of how these are affected by the architecture and type of norm constraint, for t…