1.9k citations · 6.5k across the 132 of their papers we have counts for
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Optimistic Rates: A Unifying Theory for Interpolation Learning and Regularization in Linear Regression
Lijia Zhou, Frederic Koehler, Danica J. Sutherland +1
We study a localized notion of uniform convergence known as an "optimistic rate" (Panchenko 2002; Srebro et al. 2010) for linear regression with Gaussian data. Our refined analysis…
Exponential Family Model-Based Reinforcement Learning via Score Matching
Gene Li, Junbo Li, Anmol Kabra +3
We propose an optimistic model-based algorithm, dubbed SMRL, for finite-horizon episodic reinforcement learning (RL) when the transition model is specified by exponential family di…
Transductive Robust Learning Guarantees
Omar Montasser, Steve Hanneke, Nathan Srebro
We study the problem of adversarially robust learning in the transductive setting. For classes of bounded VC dimension, we propose a simple transductive learner that…
A Stochastic Newton Algorithm for Distributed Convex Optimization
Brian Bullins, Kumar Kshitij Patel, Ohad Shamir +2
We propose and analyze a stochastic Newton algorithm for homogeneous distributed stochastic convex optimization, where each machine can calculate stochastic gradients of the same p…
On Margin Maximization in Linear and ReLU Networks
Gal Vardi, Ohad Shamir, Nathan Srebro
The implicit bias of neural networks has been extensively studied in recent years. Lyu and Li [2019] showed that in homogeneous networks trained with the exponential or the logisti…
Fast Margin Maximization via Dual Acceleration
Ziwei Ji, Nathan Srebro, Matus Telgarsky
We present and analyze a momentum-based gradient method for training linear classifiers with an exponentially-tailed loss (e.g., the exponential or logistic loss), which maximizes…