536 citations · 4.4k across the 97 of their papers we have counts for
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Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes
Ohad Shamir, Tong Zhang
Stochastic Gradient Descent (SGD) is one of the simplest and most popular stochastic optimization methods. While it has already been theoretically studied for decades, the classica…
On the Complexity of Bandit and Derivative-Free Stochastic Convex Optimization
Ohad Shamir
The problem of stochastic convex optimization with bandit feedback (in the learning community) or without knowledge of gradients (in the optimization community) has received much a…
Decoupling Exploration and Exploitation in Multi-Armed Bandits
Orly Avner, Shie Mannor, Ohad Shamir
We consider a multi-armed bandit problem where the decision maker can explore and exploit different arms at every round. The exploited arm adds to the decision maker's cumulative r…
Relax and Localize: From Value to Algorithms
Alexander Rakhlin, Ohad Shamir, Karthik Sridharan
We show a principled way of deriving online learning algorithms from a minimax analysis. Various upper bounds on the minimax value, previously thought to be non-constructive, are s…