536 citations · 4.4k across the 97 of their papers we have counts for
11 papers · 1 filter
Replay For Safety
Liran Szlak, Ohad Shamir
Experience replay \citep{lin1993reinforcement, mnih2015human} is a widely used technique to achieve efficient use of data and improved performance in RL algorithms. In experience r…
Convergence Results For Q-Learning With Experience Replay
Liran Szlak, Ohad Shamir
A commonly used heuristic in RL is experience replay (e.g.~\citet{lin1993reinforcement, mnih2015human}), in which a learner stores and re-uses past trajectories as if they were sam…
On the Optimal Memorization Power of ReLU Neural Networks
Gal Vardi, Gilad Yehudai, Ohad Shamir
We study the memorization power of feedforward ReLU neural networks. We show that such networks can memorize any points that satisfy a mild separability assumption using $\tild…
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…
Random Shuffling Beats SGD Only After Many Epochs on Ill-Conditioned Problems
Itay Safran, Ohad Shamir
Recently, there has been much interest in studying the convergence rates of without-replacement SGD, and proving that it is faster than with-replacement SGD in the worst case. Howe…