10 citations · 17 across the 4 of their papers we have counts for
4 papers
Better Best of Both Worlds Bounds for Bandits with Switching Costs
Idan Amir, Guy Azov, Tomer Koren +1
We study best-of-both-worlds algorithms for bandits with switching cost, recently addressed by Rouyer, Seldin and Cesa-Bianchi, 2021. We introduce a surprisingly simple and effecti…
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…
Never Go Full Batch (in Stochastic Convex Optimization)
Idan Amir, Yair Carmon, Tomer Koren +1
We study the generalization performance of optimization algorithms for stochastic convex optimization: these are first-order methods that only access the exact…
SGD Generalizes Better Than GD (And Regularization Doesn't Help)
Idan Amir, Tomer Koren, Roi Livni
We give a new separation result between the generalization performance of stochastic gradient descent (SGD) and of full-batch gradient descent (GD) in the fundamental stochastic co…